{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "85c59384-5a02-4804-916c-bb86ab8e08e9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Obtaining file:///dss/dsshome1/04/di93zer/git/cellnet\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hInstalling collected packages: cellnet\n",
      "  Running setup.py develop for cellnet\n",
      "Successfully installed cellnet\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.1.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3 -m pip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install -e /dss/dsshome1/04/di93zer/git/cellnet --no-deps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c658b1f-fff4-459e-9d41-3c3994ab0135",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%load_ext autoreload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da81cedd-cb71-43f4-a308-5e9b5c39be30",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "torch.set_float32_matmul_precision('high')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95173974-6afc-4008-9200-87bc4d79b8de",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from os.path import join\n",
    "import yaml\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import lightning.pytorch as pl\n",
    "import dask.dataframe as dd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from cellnet.estimators import EstimatorCellTypeClassifier\n",
    "from cellnet.models import TabnetClassifier, LinearClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6d21a22d-59e2-433f-b475-65a8eb9b3aaa",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "DATA_PATH = '/mnt/dssmcmlfs01/merlin_cxg_2023_05_15_sf-log1p'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a1a82cf-6a8c-4164-ae5d-6d4443aa5d9a",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Scaling with respect to training data size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5c08cf6-2fd6-4fee-99cb-00600c1ca21f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "from utils import get_best_ckpts, correct_labels\n",
    "import gc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "840b782c-d55c-4b0d-98b4-2a7f64bae3d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "LOGS_PATH = '/mnt/dssfs02/tb_logs/cxg_2023_05_15_tabnet/default'\n",
    "\n",
    "\n",
    "CKPTS_DONOR_SUBSAMPLING = {\n",
    "    'subset_15': get_best_ckpts(LOGS_PATH, [f'subset_15_{i}' for i in range(1, 5)]),\n",
    "    'subset_30': get_best_ckpts(LOGS_PATH, [f'subset_30_{i}' for i in range(1, 5)]),\n",
    "    'subset_50': get_best_ckpts(LOGS_PATH, [f'subset_50_{i}_new3' for i in range(1, 5)]),  # Rerun cause this was not trained long enough\n",
    "    'subset_70': get_best_ckpts(LOGS_PATH, [f'subset_70_{i}' for i in range(1, 5)]),\n",
    "    'subset_100': get_best_ckpts(LOGS_PATH, [f'w_augment_{i}' for i in range(1, 5)])\n",
    "}\n",
    "CKPTS_RANDOM_SUBSAMPLING = {\n",
    "    'subsample_15': get_best_ckpts(LOGS_PATH, [f'random_subsample_15_{i}' for i in range(1, 5)]),\n",
    "    'subsample_30': get_best_ckpts(LOGS_PATH, [f'random_subsample_30_{i}_new' for i in range(1, 5)]),  # Rerun cause this was trained too long\n",
    "    'subsample_50': get_best_ckpts(LOGS_PATH, [f'random_subsample_50_{i}_new3' for i in range(1, 5)]),  # Rerun cause this was not trained long enough\n",
    "    'subsample_70': get_best_ckpts(LOGS_PATH, [f'random_subsample_70_{i}' for i in range(1, 5)]),\n",
    "    'subsample_100': get_best_ckpts(LOGS_PATH, [f'w_augment_{i}' for i in range(1, 5)])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4f723057-c9b6-4537-af64-c403fa6b3e4a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def millions_formatter(x, pos):\n",
    "    return f'{int(x / 1000000)}M'\n",
    "\n",
    "\n",
    "formatter = ticker.FuncFormatter(millions_formatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86d17070-839d-4a60-87d1-5e7f3d3c9205",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Training data size vs number of donors (donor based subsampling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "769aab34-ee0a-417b-8b86-7c3dadacde45",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "path_template = '/mnt/dssmcmlfs01/merlin_cxg_2023_05_15_sf-log1p{subsample}'\n",
    "\n",
    "\n",
    "n_donors = []\n",
    "n_train_samples = []\n",
    "\n",
    "for subsample in ['_subsample_15', '_subsample_30', '_subsample_50', '_subsample_70', '']:\n",
    "    tech_samples = (\n",
    "        dd.read_parquet(join(path_template.format(subsample=subsample), 'train'), columns='tech_sample')\n",
    "        .compute()\n",
    "    )\n",
    "    n_donors.append(tech_samples.nunique())\n",
    "    n_train_samples.append(len(tech_samples))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59ca3a99-cbd3-4499-91aa-2420b6dd097c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1c4e03dc-03e0-4341-93a7-d09a93ed09ec",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Overall statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8048bc7-575d-410a-83e6-efa4ed11e87c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import gc\n",
    "\n",
    "\n",
    "n_training_samples = []\n",
    "subsampling = []\n",
    "f1_score = []\n",
    "loss = []\n",
    "ckpt_list = []\n",
    "\n",
    "\n",
    "estim = EstimatorCellTypeClassifier(DATA_PATH)\n",
    "estim.init_datamodule(batch_size=4096)\n",
    "estim.trainer = pl.Trainer(logger=[], accelerator='gpu', devices=1)\n",
    "\n",
    "\n",
    "try:\n",
    "    res_df = pd.read_csv('data_scaling.csv')\n",
    "except FileNotFoundError:\n",
    "    res_df = pd.DataFrame({\n",
    "        'n_training_samples': [],\n",
    "        'subsamping_strategy': [],\n",
    "        'f1_score': [],\n",
    "        'loss': [],\n",
    "        'ckpt': []\n",
    "    })\n",
    "\n",
    "\n",
    "for sampling_strategy, ckpts_dict in [('donor_based', CKPTS_DONOR_SUBSAMPLING), ('random', CKPTS_RANDOM_SUBSAMPLING)]:\n",
    "    for _, ckpts in ckpts_dict.items():\n",
    "        for ckpt in ckpts:\n",
    "            if ckpt in res_df.ckpt.tolist():\n",
    "                # use cached data if available\n",
    "                run = (res_df.ckpt == ckpt) & (res_df.subsamping_strategy == sampling_strategy)\n",
    "                f1_ckpt = float(res_df.loc[run, 'f1_score'])\n",
    "                loss_ckpt = float(res_df.loc[run, 'loss'])\n",
    "                print(\n",
    "                    f\"Reusing {ckpt.split('/')[-1]} checkpoint: f1-score={f1_ckpt:.4f} loss={loss_ckpt:.4f}\"\n",
    "                )\n",
    "                subsampling.append(sampling_strategy)\n",
    "                ckpt_list.append(ckpt)\n",
    "                n_training_samples.append(int(res_df.loc[run, 'n_training_samples']))\n",
    "                f1_score.append(float(res_df.loc[run, 'f1_score']))\n",
    "                loss.append(float(res_df.loc[run, 'loss']))\n",
    "            else:\n",
    "                subsampling.append(sampling_strategy)\n",
    "                ckpt_list.append(ckpt)\n",
    "                with open(join(ckpt.split('checkpoints')[0], 'hparams.yaml')) as f:\n",
    "                    hparams = yaml.full_load(f.read())\n",
    "                    n_training_samples.append(hparams['train_set_size'])\n",
    "\n",
    "                estim.model = TabnetClassifier.load_from_checkpoint(ckpt, **estim.get_fixed_model_params('tabnet'))\n",
    "                res = estim.test()[0]\n",
    "                f1_score.append(res['test_f1_macro'])\n",
    "                loss.append(res['test_loss'])\n",
    "                gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4167c3b-9116-47ba-92eb-3d7197e53384",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res_df = pd.DataFrame({\n",
    "    'n_training_samples': n_training_samples,\n",
    "    'subsamping_strategy': subsampling,\n",
    "    'f1_score': f1_score,\n",
    "    'loss': loss,\n",
    "    'ckpt': ckpt_list\n",
    "})\n",
    "res_df.to_csv('data_scaling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "efde7f83-ead3-4915-a9ef-3b0ad88aedde",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res_df['subsamping_strategy'] = res_df['subsamping_strategy'].replace({\n",
    "    'donor_based': 'donor-based sampling',\n",
    "    'random': 'cell-based sampling',\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e08e2403-0b06-4a48-a6eb-3123bf9ba0c5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_2890291/1886027191.py:35: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax2.set_yticklabels([f'{tick:.2f}' for tick in ax2.get_yticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(5.5, 5.), sharex=True)\n",
    "\n",
    "\n",
    "sns.lineplot(\n",
    "    x='n_training_samples',\n",
    "    y='f1_score',\n",
    "    hue='subsamping_strategy',\n",
    "    marker='o',\n",
    "    palette=sns.color_palette()[2:4],\n",
    "    data=res_df,\n",
    "    ax=ax1\n",
    ")\n",
    "ax1.xaxis.set_major_formatter(formatter)\n",
    "ax1.set_xlabel('number of training samples (in millions)')\n",
    "ax1.set_ylabel('F1-score (macro avg.)')\n",
    "ax1.set_yticks([0.73, 0.76, 0.79, 0.82])\n",
    "ax1.legend(title='')\n",
    "ax1_upper = ax1.twiny()\n",
    "ax1_upper.set_xlim(ax1.get_xlim())\n",
    "ax1_upper.set_xticks(n_train_samples)\n",
    "ax1_upper.set_xticklabels(n_donors)\n",
    "ax1_upper.set_xlabel('number of unique donors (donor-based sampling)')\n",
    "\n",
    "sns.lineplot(\n",
    "    x='n_training_samples',\n",
    "    y='loss',\n",
    "    hue='subsamping_strategy',\n",
    "    marker='o',\n",
    "    palette=sns.color_palette()[2:4],\n",
    "    data=res_df,\n",
    "    ax=ax2\n",
    ")\n",
    "ax2.xaxis.set_major_formatter(formatter)\n",
    "ax2.set_xlabel('number of training samples (in millions)')\n",
    "ax2.set_yticklabels([f'{tick:.2f}' for tick in ax2.get_yticks()])\n",
    "ax2.set_ylabel('Loss (cross entropy)')\n",
    "ax2.legend(title='')\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('/dss/dsshome1/04/di93zer/git/cellnet/figure-plots/figure2/donor_vs_random_sampling.pdf')\n",
    "plt.savefig('/dss/dsshome1/04/di93zer/git/cellnet/figure-plots/figure2/donor_vs_random_sampling.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f8cb66a-26a0-4401-b259-fb994332792b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "896335d2-b317-45e4-89a2-fb50a0785041",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Statistics grouped by BioNetwork"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7512a485-7d70-4bd3-bbee-dec26fbd39df",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cell_type_mapping = pd.read_parquet(join(DATA_PATH, 'categorical_lookup/cell_type.parquet'))\n",
    "tissue_general_mapping = pd.read_parquet(join(DATA_PATH, 'categorical_lookup/tissue_general.parquet'))\n",
    "cell_type_hierarchy = np.load(join(DATA_PATH, 'cell_type_hierarchy/child_matrix.npy'))\n",
    "tissue_general = dd.read_parquet(join(DATA_PATH, 'test'), columns='tissue_general').compute().to_numpy()\n",
    "y_true = dd.read_parquet(join(DATA_PATH, 'test'), columns='cell_type').compute().to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dcb6777-fed8-4582-99a8-e0ace23f6613",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "preds = []\n",
    "n_training_samples = []\n",
    "ckpt_list = []\n",
    "\n",
    "\n",
    "estim = EstimatorCellTypeClassifier(DATA_PATH)\n",
    "estim.init_datamodule(batch_size=4096)\n",
    "estim.trainer = pl.Trainer(logger=[], accelerator='gpu', devices=1)\n",
    "\n",
    "\n",
    "for subset, ckpts in CKPTS_DONOR_SUBSAMPLING.items():\n",
    "    for ckpt in ckpts:\n",
    "        ckpt_list.append(ckpt)\n",
    "        with open(join(ckpt.split('checkpoints')[0], 'hparams.yaml')) as f:\n",
    "            hparams = yaml.full_load(f.read())\n",
    "            n_training_samples.append(hparams['train_set_size'])\n",
    "\n",
    "        estim.model = TabnetClassifier.load_from_checkpoint(ckpt, **estim.get_fixed_model_params('tabnet'))\n",
    "        probas = estim.predict(estim.datamodule.test_dataloader())\n",
    "        preds.append(correct_labels(y_true, np.argmax(probas, axis=1), cell_type_hierarchy))\n",
    "        gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c1d842fd-515d-43a9-a891-c977f37fbbc5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "pickle.dump((preds, n_training_samples, ckpt_list), open('preds.pickle', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a4d9ff16-47ca-4b12-890e-4ad921931c23",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "\n",
    "with open('preds.pickle', 'rb') as f:\n",
    "    preds, n_training_samples, ckpt_list = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "adfc0201-e48f-4b4f-b7a1-0631791d395c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%autoreload\n",
    "from utils import BIONETWORK_GROUPING\n",
    "from typing import Dict, List\n",
    "\n",
    "\n",
    "def macro_f1_per_group(y_true, y_pred, group_variable, grouping: Dict[str, List[str]]):\n",
    "    assert len(y_true) == len(y_pred) == len(group_variable)\n",
    "    groups = []\n",
    "    f1_macro = []\n",
    "\n",
    "    for group, group_assignments in grouping.items():\n",
    "        y_pred_group = y_pred[np.isin(group_variable, group_assignments).squeeze()]\n",
    "        y_true_group = y_true[np.isin(group_variable, group_assignments).squeeze()]\n",
    "        clf_report = pd.DataFrame(classification_report(\n",
    "            y_true=y_true_group,\n",
    "            y_pred=y_pred_group,\n",
    "            labels=np.unique(y_true_group),\n",
    "            output_dict=True,\n",
    "            zero_division=0\n",
    "        )).T\n",
    "        groups.append(group)\n",
    "        f1_macro.append(clf_report.loc['macro avg', 'f1-score'])\n",
    "\n",
    "    return pd.DataFrame({'group': groups, 'f1_score': f1_macro})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea07a63c-00e1-40a7-a000-951199705d2b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tissue = tissue_general_mapping.loc[tissue_general].to_numpy().flatten()\n",
    "res = {'bionetwork': [], 'f1_score': [], 'n_training_samples': []}\n",
    "\n",
    "\n",
    "for y_pred, training_samples in zip(preds, n_training_samples):\n",
    "    f1_score_per_bionetwork = macro_f1_per_group(y_true, y_pred, tissue, BIONETWORK_GROUPING)\n",
    "    res['bionetwork'] += f1_score_per_bionetwork.group.tolist()\n",
    "    res['f1_score'] += f1_score_per_bionetwork.f1_score.tolist()\n",
    "    res['n_training_samples'] += [training_samples] * len(f1_score_per_bionetwork)\n",
    "\n",
    "res = pd.DataFrame(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1539ae26-1496-4e5c-aafe-416c1f53bec1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res.to_csv('model_eval_cache/model_scaling_per_bionetwork.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "61c02fe2-c47e-4c5e-8680-bbf8f44d3142",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res = pd.read_csv('model_eval_cache/model_scaling_per_bionetwork.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28a730dc-ae99-46d9-bc63-166f215d861a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res['bionetwork'] = (\n",
    "    res['bionetwork'].str.replace('_', ' ')\n",
    "    .replace({\n",
    "        'oral and craniofacial': 'oral+craniofacial',\n",
    "        'blood and immune': 'blood+immune'\n",
    "    })\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d450b7dd-51a3-4940-a50e-b73401b0505e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_1525030/3864536061.py:5: UserWarning: The palette list has more values (20) than needed (14), which may not be intended.\n",
      "  sns.lineplot(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scanpy.plotting.palettes import default_20\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(4., 3.))\n",
    "sns.lineplot(\n",
    "    x='n_training_samples', \n",
    "    y='f1_score', \n",
    "    hue='bionetwork', \n",
    "    hue_order=(\n",
    "        res[res.bionetwork != 'adipose']\n",
    "        .query('n_training_samples == 15240192')\n",
    "        .groupby('bionetwork').mean()\n",
    "        .sort_values('f1_score', ascending=False)\n",
    "        .index.tolist()\n",
    "    ),\n",
    "    marker='o', \n",
    "    linestyle=':',\n",
    "    err_style='band',\n",
    "    palette=default_20,\n",
    "    data=res[res.bionetwork != 'adipose'],\n",
    "    ax=ax\n",
    ")\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "ax.set_xlabel('number of training samples (in millions)')\n",
    "# ax.set_ylim(0. ,1.)\n",
    "ax.set_ylabel('F1-score (macro avg.)')\n",
    "ax.legend(title='Organ System')\n",
    "\n",
    "ax2 = ax.twiny()\n",
    "ax2.set_xlim(ax.get_xlim())\n",
    "ax2.set_xticks(n_train_samples)\n",
    "ax2.set_xticklabels(n_donors)\n",
    "ax2.set_xlabel('number of unique donors')\n",
    "sns.move_legend(ax, 'upper left', bbox_to_anchor=(1, 0.95))\n",
    "\n",
    "handles, labels = ax.get_legend_handles_labels()\n",
    "lgd = ax.legend(handles, labels, loc='upper center', bbox_to_anchor=(1.3, 1.15))\n",
    "\n",
    "plt.savefig(\n",
    "    '/dss/dsshome1/04/di93zer/git/cellnet/figure-plots/figure2/scaling_per_organ_system.pdf',\n",
    "    bbox_extra_artists=(lgd, ), \n",
    "    bbox_inches='tight'\n",
    ")\n",
    "plt.savefig(\n",
    "    '/dss/dsshome1/04/di93zer/git/cellnet/figure-plots/figure2/scaling_per_organ_system.png', \n",
    "    dpi=300,\n",
    "    bbox_extra_artists=(lgd, ), \n",
    "    bbox_inches='tight'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e07704a2-f6e8-4358-9abe-0dbf192fa466",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8db2067e-e7e2-4672-ad9d-f85870f9dbb1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "f1_scores_small = (\n",
    "    res\n",
    "    .query('n_training_samples == 2108416')\n",
    "    [['bionetwork', 'f1_score']]\n",
    "    .groupby('bionetwork')['f1_score']\n",
    "    .agg(['mean', 'std'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7cb89b6f-abee-42da-8e51-37952892b5e1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "f1_scores_big = (\n",
    "    res\n",
    "    .query('n_training_samples == 15240192')\n",
    "    [['bionetwork', 'f1_score']]\n",
    "    .groupby('bionetwork')['f1_score']\n",
    "    .agg(['mean', 'std'])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4d6f73b-e27c-4e47-8ba0-16f5e37caa97",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8b8f9fc-ce60-4a42-88e3-b43fe2723274",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy.stats import bootstrap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09a92e71-356c-4254-98a1-fb08d733cab0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median: 0.1219±0.0212\n"
     ]
    }
   ],
   "source": [
    "median = (f1_scores_big - f1_scores_small)['mean'].median()\n",
    "median_std = bootstrap(\n",
    "    ((f1_scores_big - f1_scores_small)['mean'].to_numpy(), ), \n",
    "    np.median, \n",
    "    random_state=1\n",
    ").standard_error\n",
    "\n",
    "print(f'Median: {median:.4f}±{median_std:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "28dacd97-d64d-4ffb-b9a7-a50185890c97",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0454±0.0083\n"
     ]
    }
   ],
   "source": [
    "minimum = (f1_scores_big - f1_scores_small)['mean'].min()\n",
    "minimum_std = bootstrap(\n",
    "    ((f1_scores_big - f1_scores_small)['mean'].to_numpy(), ), \n",
    "    np.min, \n",
    "    random_state=1\n",
    ").standard_error\n",
    "\n",
    "print(f'{minimum:.4f}±{minimum_std:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8ee8bee-d9a2-44f5-853f-1da4204fcee9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "742c2963-7766-4b00-9172-d3cf1a463fa6",
   "metadata": {
    "tags": []
   },
   "source": [
    "## TabNet vs Linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d79d16eb-a3c9-4cac-baad-a6b006641a38",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "LOGS_PATH_TABNET = '/mnt/dssfs02/tb_logs/cxg_2023_05_15_tabnet/default'\n",
    "LOGS_PATH_LINEAR = '/mnt/dssfs02/tb_logs/cxg_2023_05_15_linear/default'\n",
    "\n",
    "\n",
    "CKPTS_TABNET = {\n",
    "    'tabnet_15': get_best_ckpts(LOGS_PATH_TABNET, [f'subset_15_{i}_augmentation=False' for i in range(1, 4)]),\n",
    "    'tabnet_30': get_best_ckpts(LOGS_PATH_TABNET, [f'subset_30_{i}_augmentation=False' for i in range(1, 4)]),\n",
    "    'tabnet_50': get_best_ckpts(LOGS_PATH_TABNET, [f'subset_50_{i}_augmentation=False' for i in range(1, 4)]),\n",
    "    'tabnet_70': get_best_ckpts(LOGS_PATH_TABNET, [f'subset_70_{i}_augmentation=False' for i in range(1, 4)]),\n",
    "    'tabnet_100': get_best_ckpts(LOGS_PATH_TABNET, [f'w_augment_{i}' for i in range(1, 4)])\n",
    "}\n",
    "CKPTS_LINEAR = {\n",
    "    'linear_15': get_best_ckpts(LOGS_PATH_LINEAR, [f'subset_15_{i}' for i in range(1, 4)]),\n",
    "    'linear_30': get_best_ckpts(LOGS_PATH_LINEAR, [f'subset_30_{i}' for i in range(1, 4)]),\n",
    "    'linear_50': get_best_ckpts(LOGS_PATH_LINEAR, [f'subset_50_{i}' for i in range(1, 4)]),\n",
    "    'linear_70': get_best_ckpts(LOGS_PATH_LINEAR, [f'subset_70_{i}' for i in range(1, 4)]),\n",
    "    'linear_100': get_best_ckpts(\n",
    "        '/mnt/dssfs02/tb_logs/juwles/cxg_2023_05_15_linear/default',\n",
    "        [f'version_{i}' for i in range(1, 4)]\n",
    "    )\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2835f7ab-ccc0-4bfc-bf87-5ac3c2ac5979",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "cell_type_hierarchy = np.load(join(DATA_PATH, 'cell_type_hierarchy/child_matrix.npy'))\n",
    "y_true = dd.read_parquet(join(DATA_PATH, 'test'), columns='cell_type').compute().to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69e7aa69-fcab-48dd-9769-61a519689222",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import gc\n",
    "\n",
    "\n",
    "n_training_samples = []\n",
    "models = []\n",
    "f1_score = []\n",
    "ckpt_list = []\n",
    "\n",
    "\n",
    "try:\n",
    "    res_df = pd.read_csv('linear_vs_tabnet.csv')\n",
    "except FileNotFoundError:\n",
    "    res_df = pd.DataFrame({\n",
    "        'n_training_samples': [],\n",
    "        'model': [],\n",
    "        'f1_score': [],\n",
    "        'ckpt': []\n",
    "    })\n",
    "\n",
    "    \n",
    "estim = EstimatorCellTypeClassifier(DATA_PATH)\n",
    "estim.init_datamodule(batch_size=4096)\n",
    "estim.trainer = pl.Trainer(logger=[], accelerator='gpu', devices=1)\n",
    "\n",
    "\n",
    "for model, ckpts_dict in [('linear', CKPTS_LINEAR), ('tabnet', CKPTS_TABNET)]:\n",
    "    for _, ckpts in ckpts_dict.items():\n",
    "        for ckpt in ckpts:\n",
    "            if ckpt in res_df.ckpt.tolist():\n",
    "                # use cached data if available\n",
    "                run = (res_df.ckpt == ckpt) & (res_df.model == model)\n",
    "                f1_ckpt = float(res_df.loc[run, 'f1_score'])\n",
    "                print(\n",
    "                    f\"Reusing {ckpt.split('/')[-1]} checkpoint: f1-score={f1_ckpt:.4f}\"\n",
    "                )\n",
    "                models.append(model)\n",
    "                ckpt_list.append(ckpt)\n",
    "                n_training_samples.append(int(res_df.loc[run, 'n_training_samples']))\n",
    "                f1_score.append(float(res_df.loc[run, 'f1_score']))\n",
    "            else:\n",
    "                models.append(model)\n",
    "                ckpt_list.append(ckpt)\n",
    "                with open(join(ckpt.split('checkpoints')[0], 'hparams.yaml')) as f:\n",
    "                    hparams = yaml.full_load(f.read())\n",
    "                    n_training_samples.append(hparams['train_set_size'])\n",
    "                \n",
    "                if model == 'tabnet':\n",
    "                    estim.model = TabnetClassifier.load_from_checkpoint(ckpt, **estim.get_fixed_model_params('tabnet'))\n",
    "                elif model == 'linear':\n",
    "                    estim.model = LinearClassifier.load_from_checkpoint(ckpt, **estim.get_fixed_model_params('linear'))\n",
    "\n",
    "                probas = estim.predict(estim.datamodule.test_dataloader())\n",
    "                y_pred = correct_labels(y_true, np.argmax(probas, axis=1), cell_type_hierarchy)\n",
    "                clf_report = pd.DataFrame(classification_report(y_true, y_pred, output_dict=True, zero_division=0)).T\n",
    "                f1_score.append(float(clf_report.loc['macro avg', 'f1-score']))\n",
    "                gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "7b89e946-e3a9-4d0f-a3fc-9fe6f1a0a682",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res_df = pd.DataFrame({\n",
    "    'n_training_samples': n_training_samples,\n",
    "    'model': models,\n",
    "    'f1_score': f1_score,\n",
    "    'ckpt': ckpt_list\n",
    "})\n",
    "res_df.to_csv('model_eval_cache/linear_vs_tabnet.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6beacd28-f2c6-4df3-a379-94f0a6815aa8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res_df = pd.read_csv('model_eval_cache/linear_vs_tabnet.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2616699-1533-41bf-bcc6-30726caa25a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3551ad51-4247-4759-8c1d-f565fef374cc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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u0PHjxwkICKBhw4Y8+eSTxMXFlXpeVlYWS5cuJSwsjODg4BquZe2KjY0lISGB/v37m8pcXV3p2rUrO3bsAGD//v2cP38eCwsL2rdvj7+/P/fccw9HjhyprWrXe4mJiaxZs4Znn33WVFae96FZs2Z4enqyePFi8vPzycnJYfHixbRo0YLQ0NBaaEndk5aWBoCHh4epLDs7myFDhrBgwYJybZT31Vdf4eDgwCOPPGIqW716NZ06dWLu3LkEBgbStGlTXn75ZXJycqq+EaLOMxgMrFixgqysLLp162Yq/+abb/Dy8qJVq1ZMmzaN7Ozsa97jwoULrFy5kt69e5vKqutzJgHHDejatSvLli1j7dq1LFy4kNjYWG6//XYyMjJM5/znP//ByckJJycnfv/9d/78889r7jJZXyUkJADg6+trVu7r62s6durUKUDLi3nttdf49ddfcXd3p0+fPrKLZTVZvnw5zs7OPPTQQ6ay8rwPzs7ObNq0ia+//hp7e3ucnJxYu3Ytv//+O1ZWsrSP0WjkpZdeokePHrRq1cpUPnHiRLp3784DDzxQrvssXryYIUOGYG9vbyo7deoUW7du5ciRI6xatYr58+fz008/MWbMmCpvh6i7/vnnH5ycnLC1tWX06NGsWrXKtNPwkCFD+Prrr9m4cSPTpk3jv//9L0899VSJezzxxBM4ODgQGBiIi4sLX375pelYtX3OqrS/5BZ3+fJl5eLiYtYdnZqaqmJiYtTmzZvVwIEDVYcOHVROTk6Ja+vTEAFXdRVv27ZNAerChQtm5z366KPqscceU0op9c033yhAffbZZ6bjubm5ysvLSy1atKjEc9Sn16u6XP0+XK1Zs2Zq3LhxZmXleR+ys7NVly5d1NChQ9Xu3bvVjh071MMPP6xatmypsrOzq6UtN5PRo0erkJAQdfbsWVPZL7/8oho3bqwyMjJMZWW9P9u3b1eA2rt3r1n5nXfeqezs7FRqaqqp7Oeff1Y6nU5e+1tIXl6eOn78uNq7d6+aOnWq8vLyUkePHi313PXr15c6lB8fH68iIyPVL7/8osLDw9ULL7xgOlZdnzPp4ahCbm5uNG3alBMnTpjKXF1dadKkCb169eKnn34iKiqKVatW1WIta15R93FiYqJZeWJioumYv78/gClKB7C1taVhw4bXHKYSlff3338THR3Nc889Z1Zenvfh22+/5fTp0yxdupTOnTtz22238e233xIbG8svv/xSc42og8aNG8evv/7Kxo0bCQoKMpVv2LCBkydP4ubmhpWVlakn6OGHH6ZPnz4l7vPll1/Srl07OnbsaFbu7+9PYGAgrq6uprIWLVqglOLcuXPV0yhR59jY2NC4cWM6duzI7Nmzadu2LR9//HGp53bt2hXA7HsJtN/LzZs35/777+ezzz5j4cKFxMfHA9X3OZOAowplZmZy8uRJ0y/tqymlUEqRl5dXwzWrXWFhYfj5+bF+/XpTWXp6Ort27TKNOxZNdS0+lbCgoIDTp08TEhJS43Wu7xYvXkzHjh1p27atWXl53ofs7GwsLCzMZloUPTYajTXTgDpGKcW4ceNYtWoVGzZsICwszOz41KlTOXz4MAcPHjT9AHz00UcsXbrU7NzMzEx++OEHs9yaIj169ODChQtkZmaaymJiYrCwsDALcMStxWg0XvN7peizdq3vpaLrAdM9qu1zVum+EaEmTZqkNm3apGJjY9W2bdtU//79lZeXl0pKSlInT55U7777rtq7d686c+aM2rZtmxo4cKDy8PBQiYmJpnscP35cHThwQI0aNUo1bdpUHThwQB04cEDl5eXVYssqLiMjw1R3QM2bN08dOHBAnTlzRiml1Jw5c5Sbm5v65Zdf1OHDh9UDDzygwsLCzIaXJkyYoAIDA9Uff/yhoqKi1LPPPqt8fHzUpUuXTOfUl9erulzvfVBKqbS0NOXg4KAWLlxY6j2u9z5ERkYqW1tb9cILL6hjx46pI0eOqKeeekq5urqWGDa7VbzwwgvK1dVVbdq0ScXHx5t+yup+5hpDKl9++aWys7NTly9fLnEsIyNDBQUFqUceeUQdPXpUbd68WTVp0kQ999xzVdgaUZdNnTpVbd68WcXGxqrDhw+rqVOnKp1Op9atW6dOnDih3nzzTbV3714VGxurfvnlF9WwYUPVq1cv0/Vr1qxRS5YsUf/884+KjY1Vv/76q2rRooXq0aOH6Zzq+pxJwHEDBg8erPz9/ZWNjY0KDAxUgwcPNo2TnT9/Xt1zzz3Kx8dHWVtbq6CgIDVkyBAVFRVldo/evXsroMRPbGxsLbSo8jZu3FhqO4YNG6aU0qbGzpgxQ/n6+ipbW1vVr18/FR0dbXaP/Px8NWnSJOXj46OcnZ1V//791ZEjR8zOqS+vV3W53vuglFKfffaZsre3NxufLa4878O6detUjx49lKurq3J3d1d9+/ZVO3bsqM6m1WmlveaAWrp0aZnXlBZwdOvWTQ0ZMuSa10VGRqr+/fsre3t7FRQUpCIiIiR/4xYyYsQIFRISomxsbJS3t7fq16+fWrdunVJKqbi4ONWrVy/l4eGhbG1tVePGjdXkyZNVWlqa6foNGzaobt26KVdXV2VnZ6eaNGmipkyZUiLArY7PmU4p2T5RCCGEENVLcjiEEEIIUe0k4BBCCCFEtZOAQwghhBDVTgIOIYQQQlQ7CTiEEEIIUe0k4BBCCCFEtZOAo4bk5eXx+uuv33KrjJaHvDZ1g7wPtUdee1ETavtzJutw1JD09HRcXV1JS0vDxcWltqtTp8hrUzfI+1B75LUXNaG2P2fSwyGEEEKIaicBhxBCCCGqnVVtV6Au0uv1HDhwAF9fXywsqiYmy8jIAOD8+fOkp6dXyT3rC3lt6gZ5H2qPvPaiJlTH58xoNJKYmEj79u2xsio7pJAcjlLs2bOHLl261HY1hBBCiJvC7t276dy5c5nnSA9HKXx9fQHtBfT396/l2gghhBB1U3x8PF26dDF9b5ZFAo5SFA2j+Pv7ExQUVMu1EUIIIeq28qQfSNKoEEIIIaqdBBxCCCGEqHYScAghhBCi2kkOxw0wGAwUFBTUdjXqHRsbmyqbjiyEEKJukICjEpRSJCQkkJqaWttVqZcsLCwICwvDxsamtqsihBCiikjAUQlFwYaPjw8ODg7odLrarlK9YTQauXDhAvHx8TRo0EBeWyGEqAr52WBpBblpYOcKBj3YONRoFSTgqCCDwWAKNjw9PWu7OvWSt7c3Fy5cQK/XY21tXdvVEUKIm5s+F7bNh12fQW4q2LlB11FwewRY2dVYNSTgqKCinA0Hh5qNDG8lRUMpBoNBAg4hhLgR+dlasLH5vStlualXHvd4qcZ6OiQzr5Kkq7/6yGsrhBA3QJ8PKSch5k/QWWg9G6XZ9Zk2zFJDpIdDCCGEuNkYjZCfARejtZ/kGEg+DpdOQdo5KMgCn3B4YoXWo1Ga3FTITQdHrxqpsgQct5A+ffrQrl075s+fT2hoKC+99BIvvfRSbVdLCCHEtRgKIP0CJEVCcrQWVKSchNTTkJEAylj6dToL0OnAyVvL2Sgt6LBzAzuX6qv7VSTguEXt2bMHR0fH2q6GEEIIo1HrkUg+DhejCnsrThT2VsRBXsa1r7V2ALdgcA0G5wBw9tOCDAcvsLSBzGQtQbR4DkeRrqO02SqWNbMEgQQctyhvb+/argKgJeFKYqgQ4pZgKIDMREiK0gKLlBPaz+XTkBEPRv21r3XyBbcG4BIILv7g5AeO3mDjDBYWYGEJlrZgZQO2rmDrpM1AsbaHnhHaPWSWiqgNVw+p6HQ6vvjiC9asWcMff/xBYGAgH374Iffff7/pmiNHjjB58mT+/vtvHB0dueuuu/joo4/w8tLG/9auXcvbb7/NkSNHsLS0pFu3bnz88cc0atQIgNOnTxMWFsaKFSv4z3/+w65du1i0aBHPPPNMTTdfCCGqh9EI+VlwObZYb8VxuHRSy63IuXztay1ttd4KtwZab4WLvxZoOHhe6YWwsAIrWy1QsHUBW0ewsr9SZmWrDaVcrcdLGG+fhD4zBSsnTyyM+hoNNkACjiqhlCKnwFDjz2tvbVmlMzreeOMN5s6dy/vvv88nn3zCk08+yZkzZ/Dw8CA1NZW+ffvy3HPP8dFHH5GTk8OUKVN47LHH2LBhAwBZWVlERETQpk0bMjMzmTlzJg8++CAHDx40W6p86tSpfPjhh7Rv3x47u5r9wAshRJXQ50N2SmFQEQ0Xj0PKcUg9o+VcGPKvfa2DlxZUuAaBSwA4+4OjD9g5YZo8ammj/dg4aIGFjUNhQFH0U8FhEBsHjhw+zNatW+nZsydt2rSpdNMrSwKOKpBTYCB85h81/rzH3hyAg03VvYXPPPMMTzzxBADvvvsu//73v9m9ezd33303n376Ke3bt+fdd981nb9kyRKCg4OJiYmhadOmPPzww2b3W7JkCd7e3hw7doxWrVqZyl966SUeeuihKqu3EEJUC6MRCnK0BM2kKEgpyq04CalxWsBxLRbW4BpYGFgEa4GFk482DGJROIysQ+vVsLQBG8erAgtbreeiCqet6vV6kpKS0OvLGLqpRhJwCJPiEa+joyMuLi4kJSUBcOjQITZu3IiTk1OJ606ePEnTpk05fvw4M2fOZNeuXSQnJ2M0atnTcXFxZgFHp06dqrklQghRAfp8bagjOaawx+L4ldyK9PPaSp3XYuemBRVF+RXO/uDsq5UXzSDRWWg9Epa2YOOkzQwpyq8oGgqxsKyBhtYuCTiqgL21JcfeHFArz1uVrk7e1Ol0pqAhMzOTgQMH8t57JTOd/f39ARg4cCAhISF88cUXBAQEYDQaadWqFfn55l2LMjtGCFHjjAYoyNXyKC5GakFFckzhTJCzkJkEqNKv1VlowYQpsCicDeLoowUMRgMopfVGmBI3XcDW+UpvhbW9duwW3glbAo4qoNPpqnRooy7q0KEDP//8M6GhoVhZlWxrSkoK0dHRfPHFF9x+++0AbN26taarKYS4lSml5U7kZZhPMU05DpfPFC6IlX3t622cwD1EGwJxbaAlbTr7gr2Hdu+iHovKJm7e4ur3t6SoMmPHjuWLL77giSee4JVXXsHDw4MTJ06wYsUKvvzyS9zd3fH09OTzzz/H39+fuLg4pk6dWtvVFkLUR0aDNsyRkaDlViRHw8UYuHwKUs9C5nUWxHLyu9Jb4RZUmLTpq/VCGAu04AKKJW7aa1NNbzRx8xYnAYcol4CAALZt28aUKVO46667yMvLIyQkhLvvvhsLCwt0Oh0rVqxg/PjxtGrVimbNmvHvf/+bPn361HbVhRA3o6LeivxMbWXN4st3Xz4N6eeusyCWfWHCZmFg4VqYX+HoqY2cGLSNOK+fuGkHlrJWUFWo9YBjwYIFvP/++yQkJNC2bVs++eQTunTpcs3z58+fz8KFC4mLi8PLy4tHHnmE2bNnm6ZXzp49m5UrVxIVFYW9vT3du3fnvffeo1mzZjXVpDpr06ZNpn+fPn3a7JhSJccuU1NTzR43adKElStXXvP+/fv359ixY9e8b2hoaKnPI4S4hRX1VmReLMytiNGmmF46qa2ymRGvnXMtRQtiuQZfCSyc/LThEWNBscBCEjdrW60GHN9//z0REREsWrSIrl27Mn/+fAYMGEB0dDQ+Pj4lzv/222+ZOnUqS5YsoXv37sTExPDMM8+g0+mYN28eAJs3b2bs2LF07twZvV7Pq6++yl133cWxY8ckWVEIIWpDUW9FQTZcKlwQ62J04UyQM1rS5rU2GIMrC2IVBRVFC2M5eWuBhCH/GombnlqPRfHeCiu7WzpxszbVasAxb948nn/+eYYPHw7AokWLWLNmDUuWLCl1/H/79u306NGDIUOGANpfzE888QS7du0ynbN27Vqza5YtW4aPjw/79u2jV69e1dgaIYS4xRn0YMiD7EtXEjYvRmszQVLPQsb5Kz0OpXHwLJZb0QBcgrTETTtXbdlvfb554qaFlRZAOPlJ4uZNoNYCjvz8fPbt28e0adNMZRYWFvTv358dO3aUek337t35+uuv2b17N126dOHUqVP89ttvPP3009d8nrS0NAA8PDyqtgFCCHErUgr0edowSOqZwt6Kopkgp7WZIOVdEMuUYxGkDY1YWGu9FYaiwEKn5VYotGEQF7eSC2NJ4uZNo9YCjuTkZAwGA76+vmblvr6+REVFlXrNkCFDSE5OpmfPniil0Ov1jB49mldffbXU841GIy+99BI9evQwW3jqanl5eeTl5ZkeZ2SUkYgkhBC3AoNeCypy0rQVNi/GaLNBilbZTD+vBR7XYloQK9i8x8LeHZRBu7Z44mZRfpe9uyRu1lO1njRaEZs2beLdd9/lP//5D127duXEiRNMmDCBt956ixkzZpQ4f+zYsRw5cuS660HMnj2bN954o7qqLYQQdVNRb0VBjhZAJEcXzgYp3BMk7ayWzFneBbFMy3j7a70PRb0VxRM3DXlajoWjz5XETSs7sLaTxM16rtYCDi8vLywtLUlMTDQrT0xMxM/Pr9RrZsyYwdNPP81zzz0HQOvWrcnKymLkyJFMnz7dbIOwcePG8euvv7JlyxaCgoLKrMu0adOIiIgwPT5//jzh4eGVbZoQQtQtRb0VRQtiJRdOMU05qQUV5VkQq3hQUdRr4eANGLXciuKJm4YCoHBWiIOHJG4KoBYDDhsbGzp27Mj69esZNGgQoA2BrF+/nnHjxpV6TXZ2tllQAWBpqUXDRdMtlVK8+OKLrFq1ik2bNhEWFnbdutja2mJra2t6nJ6eXpkmCSFE7SmeW5GRUJhbEWU+EyQz8doLYqHT1qm4OqhwDQZrh8IppvnmiZt5GYXLdjsUS9wsvjCWJG6KK2p1SCUiIoJhw4bRqVMnunTpwvz588nKyjLNWhk6dCiBgYHMnj0b0PbqmDdvHu3btzcNqcyYMYOBAweaAo+xY8fy7bff8ssvv+Ds7ExCQgIArq6u2Nvb105DhRDiWvKztamcuWnabAyDXstfuBZDgRZY5GdpwUTRMMilU1dyK8q1IFawea+Fc4AWHOjzzBM3C3K0ngtLG0ncFDekVgOOwYMHc/HiRWbOnElCQgLt2rVj7dq1pkTSuLg4sx6N1157DZ1Ox2uvvcb58+fx9vZm4MCBvPPOO6ZzFi5cCFBihculS5fyzDPPVHubhBCi3PS5sG0+7PpMW4fCzg26joKehUO8+hzISrnSW5Eco/VWpJ8r34JYZkFF4b/t3Iv1VhRL3MxJubLipiRuimpQ60mj48aNu+YQSvGVMQGsrKyYNWsWs2bNuub9ZCXLuuP06dOEhYVx4MAB2rVrV9vVEaJuyc/Wgo3NxXZgzk3VHisFoT3gh2HlWBAr6Mry3abEzSAtOCjKrShK3NTnQ9bFwhU3bSRxU9SoWg84RN21adMm7rjjjjLP2bhxo+yXIkRFGQq0Rat2fVb68d2fQ8+XrnzxF18Qq3ivhZOP1sth6q0oTNzMvqRdW7TipiRuijpAAg5xTd27dyc+Pt70eMKECaSnp7N06VJTmSyoJkQ5FeRA6jmIXK3lXdwx/dq9F7mpkJsO938Kds5g7Visp6IwcdNogPR4LXCxlMRNUfdJeHsL+emnn2jdujX29vZ4enrSv39/srKyAFiyZAktW7bE1tYWf39/xo0bh42NDX5+fqYfe3t7bG1tTY8PHjxI3759cXNzw9PTk3/961+cPHmyxPNGRUXRvXt37OzsaNWqFZs3b67ppgtR85TSkjcTjsCGt2HpvbCwK2x4E078BY5eWs5GaezctF4JS2tt4a30C1oAYjRqiZseDcG/DQR3gZBu2vBLSDfwawnuoeDsB/Zu2hCJBBuijpAejqqgVNlz2KuLtUO5f5nEx8fzxBNPMHfuXB588EEyMjL4+++/UUqxcOFCIiIimDNnDvfccw9paWls27btuvfMysoiIiKCNm3akJmZycyZM3nwwQc5ePCgWbLv5MmTmT9/PuHh4cybN4+BAwcSGxuLp6dnpZsuRJ1kNEJeujZ75Oj/wamNkHT0yiqaoAUEYb0gM0lLEC2ew1Gk6yjIuQxuIdqskuI7mkriprhJScBRFQqy4d2Amn/eVy+ATfl2wI2Pj0ev1/PQQw8REhICaAunAbz99ttMmjSJCRMmmM7v3Lnzde/58MMPmz1esmQJ3t7eHDt2zGwp+XHjxpnOXbhwIWvXrmXx4sW88sor5aq7EHWaoUDrfUg6Bsd+gdgt2myS4qtzejbRgoyGvbQEz4Isbev17uO146XNUrG203oqhKgnJOC4RbRt25Z+/frRunVrBgwYwF133cUjjzxCQUEBFy5coF+/fhW+5/Hjx5k5cya7du0iOTkZo1FbDCguLs4s4OjWrZvp31ZWVnTq1InIyMgbb5QQtaUgV1s3I/4gRP4KZ7Zq62AU59MCwnprgYazvxZk5GVA2nntDwVHLy2vo/uLGG+fhD4zBSsnTyyMei3YEKKekYCjKlg7aL0NtfG85WRpacmff/7J9u3bWbduHZ988gnTp09n/fr1lX76gQMHEhISwhdffEFAQABGo5FWrVqRn59f6XsKUScppS20lZMK5/ZCzO9wZpu2eqeJTsurCOsFobeDo3fJIMMlSCu3d9OGSQodOXyYrVu30rNnT9q0aVPTrROiRkjAURV0unIPbdQmnU5Hjx496NGjBzNnziQkJIQ///yT0NBQ1q9ff90psMWlpKQQHR3NF198we233w5wzU3ydu7cSa9evQDQ6/Xs27fvmmuvCFFnFOVj5KTCme1w4k+I26EtuFVEZwEBHQqDjJ7aglnlDDKK0+v1JCUlodfra6RpQtQGCThuEbt27WL9+vXcdddd+Pj4sGvXLi5evEiLFi14/fXXGT16ND4+Ptxzzz1kZGSwbds2XnzxxWvez93dHU9PTz7//HP8/f2Ji4tj6tSppZ67YMECmjRpQosWLfjoo4+4fPkyI0aMqK6mClF5RfkYOZfg1GYt6fPsTshKvnKOhTUEddKCjJAeYOtcqSBDiFtNpQKOuLg4zpw5Q3Z2Nt7e3qbplKLucnFxYcuWLcyfP5/09HRCQkL48MMPueeeewDIzc3lo48+4uWXX8bLy4tHHnmkzPtZWFiwYsUKxo8fT6tWrWjWrBn//ve/S10EbM6cOcyZM4eDBw/SuHFjVq9ejZeXV3U0U4iKK8rHyLoIpzZB7GY4t0ebJVLE0hYadNVyMhrcpg1nFgUZuekSZAhRDuUOOE6fPs3ChQtZsWIF586dM1tC3MbGhttvv52RI0fy8MMPl9jRVdS+Fi1asHbt2mseHzVqFKNGjSrzHsuWLTN73L9/f44dO2ZWVvxzERoaanr8xBNPVLDGQlSTonyM3DRtV9VTm+H033B+rzaEUsTaAUK6az0ZwV20oKMgSzsn+7IEGUJUULkCjvHjx7N8+XIGDBjA22+/TZcuXQgICMDe3p5Lly5x5MgR/v77b2bOnMkbb7zB0qVLyzWtUgghakRRPkZumrba5+ktWtLnhf1a8FHE1lkbJgnrpQ2bWFhrQUbOZTAYJMgQ4gaUK+BwdHTk1KlTpS7U5OPjQ9++fenbty+zZs1i7dq1nD17VgIOIUTtKsrHyE2FS2e0ICNuhzaVVZ975Tx7dy3hM6w3BLQDnaUWZGSnFAYZThJkCFEFyhVwzJ49u9w3vPvuuytdGSGEuCFF+Rg5l+BSrDZUcnY3JBzW9iAp4uit9WKE9QLfVtpsk4IsLY/DaNT2LpEgQ4gqJbNUhBA3r+L5GFnJcPk0nN4K53ZD4hEwFptm6uxfuNpnb/BuDugKg4wkCTKEqAFVFnC8+uqrJCQksGTJkqq6ZZ1WPDlSVC15bUWZiudjZCbB5Vg4vV1L+kw6Csp45Vy3BoU9Gb3Bs7FWVpAFmYkSZAhRw6os4Dh//jxnz569/ok3OWtrbeOk7Oxs7O3ll1N1KFqp1NLSspZrIuqM4vkYGYnaMuJnd8C5fXAxCvN9SxpdWVLcPbRwc8UsyEyQIEOIWlRlAcfy5cur6lZ1mqWlJW5ubiQlJQHg4OCATrZ/rjJGo5GLFy/i4OCAlZWM+N3SiudjZCZBykk4uwvO74OU4+bnerco7Mm4HVyDrgQZGfHFgoxgbf8SCTKEqBXyG70S/Py0HRyLgg5RtSwsLGjQoIEEcrea4vkY2SmQlQLJ0doiXOf3aUMnJjrwa30l8dPJ50qQkR6vDatIkCFEnVLhgOPf//53qeU6nQ47OzsaN25Mr1696nV3uE6nw9/fHx8fHwoKCmq7OvWOjY2NLB53q7g6HyPnMlyMhgv7tE3Sim+OprPQpq2G9damsTp4aoFF/lVBhmswOHlrW73LrqtC1BkVDjg++ugjLl68SHZ2Nu7u7gBcvnwZBwcHnJycSEpKomHDhmzcuJHg4OAqr3BdYmlpWa8DKyGqhUGvBRhFK33mpkFyFJzfr80uyUi4cq6FFQQW7lsS2l0LIkoLMtwaaD0ZEmQIUWdVOOB49913+fzzz/nyyy9p1KgRACdOnGDUqFGMHDmSHj168PjjjzNx4kR++umnKq+wEOImZMrHuKzNEMlN15I9LxyAc7vMN0eztIHgroWbo3XTFt6SIEOIm16FA47XXnuNn3/+2RRsADRu3JgPPviAhx9+mFOnTjF37lwefvjhKq2oEOImUlo+Rl5aYZCxX1uMq/jmaNb20KBb4b4lXbXHEmQIUa9UOOCIj49Hr9eXKNfr9SQkaF2hAQEBZGRk3HjthBA3j6J8jLx0bepqbqq2m2pS5JUgo/jmaDZO5vuWWNlKkCFEPVbhgOOOO+5g1KhRfPnll7Rv3x6AAwcO8MILL9C3b18A/vnnH8LCwqq2pkKIuufqfIy8dMjP1IKM83vh7B5t5kgRO7fCfUt6QUB7sLS+EmRkX5IgQ4h6rMJTARYvXoyHhwcdO3bE1tYWW1tbOnXqhIeHB4sXLwbAycmJDz/8sFz3W7BgAaGhodjZ2dG1a1d2795d5vnz58+nWbNm2NvbExwczMSJE8nNzTU7p6L3FEJUQEGuNqMkKUrbcfXsLi24OL4OdnwCv4yFvz+AU5u0YMPBC1o+CP/6CJ76GXq9rPVo6HO1noz0eG2TNLcGWnlIN/BrBc5+EmwIUY9UuIfDz8+PP//8k6ioKGJiYgBo1qwZzZo1M51zxx13lOte33//PRERESxatIiuXbsyf/58BgwYQHR0ND4+PiXO//bbb5k6dSpLliyhe/fuxMTE8Mwzz6DT6Zg3b16l7imEuI6ifIy8dC25MysF9NlaT0biEYjbra2TYSw2RdzZ78qS4j4ttCmtRT0ZeZnSkyHELajCAcfWrVvp2bMnzZs3p3nz5jf05PPmzeP5559n+PDhACxatIg1a9awZMkSpk6dWuL87du306NHD4YMGQJAaGgoTzzxBLt27ar0PYUQpSgtH0OfqwUMCYchbpc2w0QZrlzjGnxlIS6vpqDTXRVkGMDaSYIMIW5RFQ44+vbtS2BgIE888QRPPfUU4eHhlXri/Px89u3bx7Rp00xlFhYW9O/fnx07dpR6Tffu3fn666/ZvXs3Xbp04dSpU/z22288/fTTlb6nEALIzwZLK8hJBTtXrRfj8iltJkl+Jlw4CHE7IOEf883RPBpeCTLcw4oFGZlXggwbCTKEEJUIOC5cuMCKFSv47rvvmDNnDm3atOHJJ5/kiSeeICgoqNz3SU5OxmAw4Ovra1bu6+tLVFRUqdcMGTKE5ORkevbsiVIKvV7P6NGjefXVVyt9T4C8vDzy8vJMj2WGjbilFOTCtvmw6zOtJ8PODbqOhG7jYNUoiP7d/HzvZleGS1wL/583CzKMYCPDJUIIcxVOGvXy8mLcuHFs27aNkydP8uijj7J8+XJCQ0NNs1Sqy6ZNm3j33Xf5z3/+w/79+1m5ciVr1qzhrbfeuqH7zp49G1dXV9NPZXtthLhpKKX1XmRdhK0fwub3tGADtP9ungvbP4H2TwE68G0Ft42FJ1bAg59BuyfBJUCb9lqU+GkslvjZQBI/hRDmbmjztrCwMKZOnUrbtm2ZMWMGmzdvLve1Xl5eWFpakpiYaFaemJho2hztajNmzODpp5/mueeeA6B169ZkZWUxcuRIpk+fXql7AkybNo2IiAjT4/Pnz0vQIeonfZ62EFf6BW0qa2hP2PV56efu/gImRcHQX8DORStTRi3IkJ4MIUQFVXqHrG3btjFmzBj8/f0ZMmQIrVq1Ys2aNeW+3sbGho4dO7J+/XpTmdFoZP369XTr1q3Ua7Kzs0ts6lW0l4lSqlL3BLC1tcXFxcX04+zsXO52CFHnKaXlZlw8DnE74dw+SDsPiUcLlxlPLf26ooW7HL2kJ0MIccMq3MMxbdo0VqxYwYULF7jzzjv5+OOPeeCBB3BwcKjwk0dERDBs2DA6depEly5dmD9/PllZWaYZJkOHDiUwMJDZs2cDMHDgQObNm0f79u3p2rUrJ06cYMaMGQwcONAUeFzvnkLcMvT5V3ozspO1x3lpcGI9xPyhBQidn9V6JkoLOuzctATSCwe1BbqkJ0MIcQMqHHBs2bKFyZMn89hjj+Hl5XVDTz548GAuXrzIzJkzSUhIoF27dqxdu9aU9BkXF2fWo/Haa6+h0+l47bXXOH/+PN7e3gwcOJB33nmn3PcUol5TqnC9jBRta/e8dNBZaruxRq7RFugq4uABKSeh6ygth+NqXUdpwy7+bSTIEELcMJ1SStV2Jeqac+fOERwczNmzZys080aIWmMo0HozMuIh8yIY8rQci9i/IXK1NnQCgA5CumsrfwZ20DZJcwuBbR9fNUtlFPSMkCCjhuzfv5/Vq1dz//3306FDh9qujqinquNzVpHvy0onjR47doy4uDjy8/PNyu+///7K3lIIUVG56VqgkXausDfDQlt2PPo3OLleC0QAbF2g+X0Qfj84+2tlBTlaIJKRBB2GYew5CX1mClbOnlgY9RJsCCGqVIUDjlOnTvHggw/yzz//oNPpKOog0el0ABgMhrIuF0LcKIMeci5pCZxZSdo6GlY2EH9Y681IOnblXK+mWm9Go75XdmPNTdNmmVjaaMGHcwA4eHLk6DHTSsJt2rSpvfYJIeqlCgccEyZMICwsjPXr1xMWFsbu3btJSUlh0qRJfPDBB9VRRyEEaEFC1kWtNyM3TevNMOTDiT8h8tcriZ8WVtDwDmg5CHzCtdU/DQXatfo8sHEGr2bg7KP1fBT+saDX60lKSkKv19daE4UQ9VeFA44dO3awYcMGvLy8sLCwwMLCgp49ezJ79mzGjx/PgQMHqqOeQtyajAZt2/aMBMhK1JYgt3bQcjUiV8PprVeWGnf0ghYPaEMnDh5aWX6WNiVWZwGOnuASpJ1nZVtrTRJC3JoqHHAYDAbTOhVeXl5cuHCBZs2aERISQnR0dJVXUIhbUn6WtjNr2nnIvQzotCGQC/vh6Cq4fPrKuQHtIXwQhPbQejeKgpT8LG1hLvdQbejE3h0sKr30jhBC3JAKBxytWrXi0KFDhIWF0bVrV+bOnYuNjQ2ff/45DRs2rI46CnFrMBq05cYzErRkzvwssHHQhkGifoXotVCQpZ1rZQdNB2iBhkeYVqbP04IUZdSGSvwagqM32DrVWpOEEKJIhQOO1157jaws7Zfem2++yb/+9S9uv/12PD09+f7776u8gkLUe/nZ2sJcaee1gAOl7bCaGqf1ZhRfO8M1WMvNaDpAO0cpbaZKXoa2OJejD7gGgoOn9lgIIeqICgccAwYMMP27cePGREVFcenSJdzd3U0zVYQQ12E0mvdmFGRpa2JY2UDMOjj2f9deO0NnAUa91ptRkKv1YHg1BSdvbQ0N+f9QCFEH3dDmbUU8PDyq4jZC1H8FOVqgkH5ey7NAaRujZWbCga9LXzujxf3gUrh2Rn62NkMFtJwM7+ZaEqi1fa00RwghyqtKAg4hRBmMRm3KamaStnZGfqa2qJadC5zZBkf/r+y1M4wGbaZJXoYWWLgGawGIvTtYWNZSo4QQomIk4BCiuhTkFm6edg6yLgEGrdfC0lobMimxdkYfLdAoWjtDn6cNqxj02iZqvq20YRNb2c1YCHHzkYBDiKqklJabUTRskl+4oqeDu9aLseuzstfOUEq7JjddC0IcvcClMAnUyqZ22yaEEDdAAg4hqoI+r3BPk/Paf416rSfC3kNbCfTqtTP822m9Gaa1M/TadQU5YO0Eno3ByUcbNpEkUCFEPVCpgOPkyZPMnz+fyMhIAMLDw5kwYQKNGjWq0soJUacppSVwZl6EjAtar4SVDdi7aUMh+5dfY+2MB8CjcM2agmzITAaMYOeu5W84eGnrbwghRD1S4YDjjz/+4P7776ddu3b06NEDgG3bttGyZUv+97//ceedd1Z5JYWoU/T5hbkZF7T1M/T5YOcMzr5wdnc51s4oTCLNy9SCEJcALQnUwVOSQIUQ9VaFA46pU6cyceJE5syZU6J8ypQpEnCI+kkpbfv3rOTCreAzwNJKW/dCnwtRv11j7YxBENjxykZrmUmFAYqLlhzq6K39Wwgh6rkKBxyRkZH88MMPJcpHjBjB/Pnzq6JOQtQdhgKtNyMjXhs6MeRpC225+EPycdi7tOy1M5TShlRy0wDLwiTQANlATZixsrLCx8cHKytJqxPVp7Y/ZxV+Vm9vbw4ePEiTJk3Myg8ePIiPj0+VVUyIWpWbfmWmSW6aNtRh7wY6Vzi1qRxrZ+gLN1DL1jZQcwvTAhA7N9lATZjJydfTrEVLvIPC8HB2IDtfj4ONBB6iatWFz1mFn+35559n5MiRnDp1iu7duwNaDsd7771HRERElVdQiBpjKJwpkpEAWUlXlg139tPKD3xdjrUzcrXeEKW0tTM8G2u9GTaOtdkyUUflFRhYtPkUS7fHkp6jx8XeiuHdwxjTpxG21pLPI6pGXfmcVTjgmDFjBs7Oznz44YdMmzYNgICAAF5//XXGjx9f5RUUotrlZVzJzchN03og7Fy1JM74g1oSaJlrZxi1/I7cDK13w9kfnAMKN1CTv1RF6XLy9SzafIqP1x83laXn6E2PR/VuKD0dtzilFEoV/rvw8ZV/F5VfOYfCcoUy/duoFF/+HVsnPmcVeha9Xs+3337LkCFDmDhxIhkZGQA4O8vKh+ImY9BDzqUrm6fpc8HaQZtpYsiH4+VYO8NQAFkXtTU4bJy1fU2cvLVgRdbOENdhaWHB0u2xpR5buj2W0X0aMfXnw2Tk6bXCq75Irv7CKfrO0cqLnWcqV2bnmH2BFbs3V92r5HWl3bvk9eb3vlYdrpxj9l/K/qItq67Fzyu6V4nzSmlD0etbni94zM4p+37XfA2KtbnU66uAh6MNW6fcUebnbOwdjavwGctWoYDDysqK0aNHm9bfkEBD3HTyMgsX6DpbmMhpoc0ScfTStoM/+M31187Iz4LcJECn9XD4BGlrZ1jb1VarRB2Xna/n2IV0/jmfxj/n08jM1TPjX+Gk5+hLPT89R09KZh4H4lKJTsyo4dqK+sLbyZaUzPwyP2cZuQV4OtVMAnuF+1G6dOnCgQMHCAkJqY76CFH1jAbzreDzs7SFtZx8AB3E7bz+2hlF98gv3EbeLUTL7bD3kCRQYebq4OKfc2mcvJiJsdifrh6ONng62eBib1Xql4GLvRVeTrbc3cqP3k29TR1mOjD1nukw70jTUVhedG6xa4qOoaPoX4XHdeb3Nh3TmZ135Ryd+T2K7qkr9hyARdH1uiuPKXY/C9NDndm9dVfVT1dKW3XF76crfsz0CmjPaVGsTTqwKPZcxetU/NrSnv/K46v+Xfz1Ldb+4q+/ebmu9DZc9ZroipUXvY5Fr83V75/F1e9rsefU6cBSp8Pb2a7Mz5mznXWJ8upS4YBjzJgxTJo0iXPnztGxY0ccHc2T4dq0aVNllRPihuRnawtzpZ3XggUKEzkdPLTejcM/Xn/tjNI2UHP01pJJxS2vKLg4fC6NI4UBxtXBRRE3B2saeTnRyMeRZr7OZOQWMLx7mNnYepHh3cMwKsXEO5vWQCtEfZaTry/zc6Y3GrGhZv5oqnDA8fjjjwOYJYjqdDqUUuh0OgwGQ9XVToiKMhqv6s3ILOzN8NbyLi5Ga70Z11s7w2wDNW9wLdxAzbLm/hoQdUtWnp5j8en8U47gwt3BmjAvJxoXBhftG7jRyMcZBxtLrC2v/HIf00fbDqK2Zw+I+svexqrOfM4qHHDExpaefFJZCxYs4P333ychIYG2bdvyySef0KVLl1LP7dOnD5s3by5Rfu+997JmzRoAMjMzmTp1Kv/3f/9HSkoKYWFhjB8/ntGjR1dpvUUdU5BzZd2M7EvazBF7V7APAmMBnNxQvrUzspK1e9k6g1cTbdjFzk2SQG8xpQUXJy5mms0GKKIFF4409nGiqa8z7YPdaOzjjL2NJTZWZf/laGttyajeDRl7R2MycgtwtrNGbzRKsCGqVF35nFU44KjK3I3vv/+eiIgIFi1aRNeuXZk/fz4DBgwgOjq61EXEVq5cSX5+vulxSkoKbdu25dFHHzWVRUREsGHDBr7++mtCQ0NZt24dY8aMISAggPvvv7/K6i7qAGPhniSZiZCeAPkZWn6Fo5fWE5GZBIdWXH/tjIJsSI/Xjtu7a7NNHL20e4l67+rg4tC5VE5dzCp1toC7gzWhXo409naiuZ8z7Rq40cjbCXsbS2ytKvfLu2hKYlHiXk11b4tbS134nFU44Jg9eza+vr6MGDHCrHzJkiVcvHiRKVOmlPte8+bN4/nnn2f48OEALFq0iDVr1rBkyRKmTp1a4nwPDw+zxytWrMDBwcEs4Ni+fTvDhg2jT58+AIwcOZLPPvuM3bt3S8BRXxTkarkZ6ech6xJg0IZFXIO04+VdOyM3TZu1Ym2nJYk6+2nHZAO1eqt4cPHP+TQOXye4CPNypJG3E838tGGRhl5acGEnPRBCVFiFA47PPvuMb7/9tkR5y5Ytefzxx8sdcOTn57Nv3z7T4mEAFhYW9O/fnx07dpTrHosXL+bxxx83S1zt3r07q1evZsSIEQQEBLBp0yZiYmL46KOPynVPUUcppeVmZCZpK3nmZWiBgqMHWNpovRSRq6+/doY+T7uHoUAbNvFtqeV32MoU7/omK0/P0cLZIkfKEVwU9Vw083WiXQN3Gno74WBjia2VhdnMASFE5VQ44EhISMDf379Eube3N/Hx8eW+T3JyMgaDAV9fX7NyX19foqKirnv97t27OXLkCIsXLzYr/+STTxg5ciRBQUFYWVlhYWHBF198Qa9eva55r7y8PPLy8kyPixY0E3WAPq8wN6NwK3ijQVs3wzVIGw5JjYNjv5RcO6PJXdpsE4+GV5JA89JBZ6klgboEaGtnWNnUavNE1ahMcNGoMLho38CdMC9HHGyssLOW4EKI6lLhgCM4OJht27YRFhZmVr5t2zYCAgKqrGLXs3jxYlq3bl0iwfSTTz5h586drF69mpCQELZs2cLYsWMJCAigf//+pd5r9uzZvPHGGzVRbVEeShXmZhQOm+RlaIGBvXthcqcBzmwvZe2MIK03w7R2RuHeKPnZ2kqgHo3AyVe7j3yp3LSuDi4OnUsltqzgwvNKQme7Bm6FwYUldlaWWFjI50CImlKpzdteeuklCgoK6Nu3LwDr16/nlVdeYdKkSeW+j5eXF5aWliQmJpqVJyYm4ufnV+a1WVlZrFixgjfffNOsPCcnh1dffZVVq1Zx3333Adq6IAcPHuSDDz64ZsAxbdo0s43nzp8/T3h4eLnbIqqIPr8wNyNe+6+hQFvvwjVAWxMjNw2OrCy5dkaDbtDqwStrZxTkaPdQRm1hLq+mWm+GjUNttk5UQvHg4p9zqRw+n3bd4KKRjxNNC3suQj0LgwtrSywluBCiVlU44Jg8eTIpKSmMGTPGNGPEzs6OKVOmmOVjXI+NjQ0dO3Zk/fr1DBo0CACj0cj69esZN25cmdf++OOP5OXl8dRTT5mVFxQUUFBQgMVVKz9aWlpiNBqveT9bW1tsba8s7Zqenl7udogqYDRA2gVtufGcS9oMEztXrTcDyrl2RuEGanmZ2pCKS6BWbu8hG6jdJIqCi8PnUrVhkfIGFz5acBHi5YCDjRX2ElwIUSdV+DexTqfjvffeY8aMGURGRmJvb0+TJk3MvrDLKyIigmHDhtGpUye6dOnC/PnzycrKMs1aGTp0KIGBgcyePdvsusWLFzNo0CA8PT3Nyl1cXOjduzeTJ0/G3t6ekJAQNm/ezFdffcW8efMqXD9RA/KztWROK2sI6qglhuZc0oZRjq+7/toZhnzzJFDvFlc2UBN1VmaenqOF61uUJ7gIKQwumvk60S7IjVAvR+xsLHGwtsTKUqaRCnEzqPSffk5OTnTu3PmGnnzw4MFcvHiRmTNnkpCQQLt27Vi7dq0pkTQuLq5Eb0V0dDRbt25l3bp1pd5zxYoVTJs2jSeffJJLly4REhLCO++8Iwt/1UX6XNg2H3Z9puVs2LlB11Fw2xj45eEr+RmlrZ2RnwnpKYCFNlPFJUib+mpVM5sQifIrEVycSyM2uezgorG3I039nGgX7E6IpyN21pYlVukUQtxcdEqVtnZe2fbu3csPP/xAXFyc2UJcoC3OdbM7d+4cwcHBnD17lqCgoNquTv2Un60FG5vfK3ms12QIaAf/m6ANmTT/l7Y+hlGvLTeenwU2juDkV7iBmrtsoFYFDh8+zNatW+nZs2el90QqHlz8c07ruThdRnAR6ulIQ29Hmhb2XIR4OWFvbVmuVTqFELWvIt+XFe7hWLFiBUOHDmXAgAGsW7eOu+66i5iYGBITE3nwwQcrXWlxi7G00no2SrP7C5gUDU/+pD3W52p7oyijNlTi2Uib2mrjWPr1osJy8vU0a9ES76AwPJwdyM7Xm1YmvJbKBhdNTMGFoym4qOwqnUKIm0eFA453332Xjz76iLFjx+Ls7MzHH39MWFgYo0aNKnV9DiFKlZN2Zbnxq+WmagmgBoO2yJeVjdaT4RxQuIGaJIFWpbwCA4s2nypzY6eM3AKOXkjX9hUpR3AR4ulIQy9Hmvo50ybQhRAvJxxMwYWsdSHErajCv7lPnjxpmnJqY2NDVlYWOp2OiRMn0rdvX1nPQlyfQa8t3mXnVnrQYeem9WTo88C7WeEGaq6ydkY1yMnXs2jzKbOtq9Nz9Hy8/jgKxR3NfIj44dD1gwtvR5r6OtM60IUQT0fTbBFZSEsIUaTCAYe7u7tpJc7AwECOHDlC69atSU1NJTs7u8orKOqhtLNa8NDledjyfsnjXUdpeRpBHbXly0W1sbSwYOn20neAXrb9NKN7NyItpwAFeDhY06Cw56KJrzOtA10J8XTAwUbruZCFtIQQZalwwNGrVy/+/PNPWrduzaOPPsqECRPYsGEDf/75J/369auOOor6JPsSpJyA2C3QY4JWtvsL81kqPSMk0KhGSilOXszk0NlUbmvkRXqOvtTz0nP0pOUUMGVAcwLc7Qj1dMTexlLLu7CW4EIIUTEVDjg+/fRTcnNzAZg+fTrW1tZs376dhx9+mNdee63KKyjqEUMBpJyEpCjY8SnErIXHvsJ4+8voMy9h5eyJhVEvwUY1yMrTs+1EMhujk9gcc5ELqbl4ONqwdcoduNhblRp0uNhb4eloyyOdgmQhLSHEDatwwFF8i3gLC4tSt5EXolSpcdrPns+1GSeuwXA5liOHD7E1KpmevXpVejqmMKeU4nhSJpuik9gQlcTe05fRG69kYVhZ6PB1seVEUibPdA/l3+tPlLjH8O5h6I1GbKwkSVcIceMq/ZskKSmJpKSkEkuGyxeGKFX2Jbh0CqJ+1YIOew/o/BzorNDb+5CUHIVeX3rXviifzKJejCitFyM+LdfsuJeTDa0CXekY4s5tDT0IdHPA1d6KZr6N0aErc5aKEELcqAoHHPv27WPYsGFERkZy9ZphOp0Og8FQZZUT9YQ+H1KOQ+JRbU8UgJ4TtYW8PJtAZkbt1u8mpZQiOjGDTdEX2RCVxP4z5r0Y1pY6mvg40TbYjS5hHrQJdMPVwRonWyvsrgokRvVuyNg7GpORW4CznTV6o1GCDSFElapwwDFixAiaNm3K4sWL8fX1lSlv4voun4HLcbD7c0BBkwHg3RRsXcE9BM4eqe0a3jQycgvYdiKZ9ZFJbIm5SGJGntlxLycbWge60qGBO10behDo7oCTrRXOtlZlJnkWLfLl6aQtDW+DrPIphKhaFQ44Tp06xc8//0zjxo2roz6ivslKhsunIOp/kH5e2ya+07OgFHg10Rb1EteklCIqIYMNUYlsjLrIgbOpGK7VixHqSesgF1ztbXC2K9mLIYQQtanCAUe/fv04dOiQBBzi+vR5kHxCG0o5tlor6/UyGPLAq5m22ZooIT23gK3HL2q9GMeTuXhVL4a3sy2tAl3o2MCdLmEeBLjZ42xrjZOdlcwmEULUWRUOOL788kuGDRvGkSNHaNWqFdbW1mbH77///iqrnLjJXT6jJYgWDaU0vw88GoKNM3iE1nbt6gylFMfi01kfmcim6IscOpdWohejqa8zbYPc6BLqQctAF1ztrXG2s8beRnoxhBA3hwoHHDt27GDbtm38/vvvJY5J0qgwybyozUqJ/D9tPxRHH+g4XEsU9Wp8y28jn5ZTwJaYi/wVmcjWE8mkZJrvuuzjbEurQFc6NHCjc2hhL4adFU62VljJFu1CiJtQhQOOF198kaeeeooZM2bg6+tbHXUSN7uCXEg+DolHIGqNVtZ7MhTkaHkbjt61W79aoJTi6IU0/jyWxKboJP45n0axTgxsLC1o6utE2yA3Ooe509zPBXdHLRfD3tpSkrOFEDe9CgccKSkpTJw4UYINUTql4HKstl/K7sLt58MfALcQbTt597BbZhO2tOwCNkYnsT4qiW0nkrmUZd6L4etiS6sAFzqEuNOhgTuBbg5aL4adFdbSiyGEqGcqHHA89NBDbNy4kUaNGlVHfcTNLusiXD4NR1dCZhI4+0OHYVoCqWeTer1sudGoOHw+jT+PJbIl5iJHL1zVi2FVrBcj1IOmfk54ONjibGeFg430Yggh6rcKBxxNmzZl2rRpbN26ldatW5dIGh0/fnyVVU7cZApyITkG4g9r+6QA9H4F8rO1ZFEnn9qtXzW4nJXH+iht+fAdJ1O4nF1gdtzPxY5WgS50CHanXYgbAa52uNjb4GRrhY2V9GIIIW4dlZql4uTkxObNm9m8ebPZMZ1OJwHHrUopbWO21LPaXikArR7W9kuxsgPPhvViKMVoVBw4m8pfxxLZcvwikfHpZr0YtlYWNPNzpm2QtoR4Ex9nPJxscLazxlF6MYQQt7AKBxyxsbHVUQ9xs8tMhNQzcOQnbbEvl0Do8DTk54Bfa7C2r+0aVlpKZh7rjiWyOfoiO0+lkJpj3osR4GpHy0AX2gW70T7YDX9Xe1zstXUxbK1k2qoQQsANbN4mhElBjjYrJf4gnPgL0EHvKZCXqSWJOt1cCcZGo2LP6Uv8FZnI38eTiU7IoPiuQXZWFjT3d6ZVoCudQtxp5OOEp6OWi+FoU/YS4kIIcasqV8AxZ84cJkyYgL399f9K3bVrF8nJydx33303XDlxEyg+lLL7C62szWPgGggWNlruxk0wjJCYnstfxxLZFHORXadSSM8137k20M2elgEutA12pW2Q1otxrY3QhBBClFSugOPYsWM0aNCARx99lIEDB9KpUye8vbW1FPR6PceOHWPr1q18/fXXXLhwga+++qpaKy3qkIwEbTXRf36AnEvg1gDaPQn5mRAQDjYOtV3DUhXojew+ncKGqCT+Pp7M8cRMs14Me2tLWvg70zLAhY4hHoR5OeLlbFuujdCEEEKUVK6A46uvvuLQoUN8+umnDBkyhPT0dCwtLbG1tSU7OxuA9u3b89xzz/HMM89gZ1d/pz6KYvKztKGUC/vh1EbQWUDvqZCbDu6h4ORX2zU0c/5yNn9FJrE5+iJ7Tl8iI8+8FyPY3Z7wABfaBrnRJtgVPxc7XOytcbGzll4MIYS4QeXO4Wjbti1ffPEFn332GYcPH+bMmTPk5OTg5eVFu3bt8PKSjbhuKUYjpJwqXOCrcCil7RPg7AcWVtqsFIvanfaZW2Bg1ymtF2PbyRROJpXsxQgPcKZlgCsdQtwI83TCw9EGFzvZCE0IIapahZNGLSwsaNeuHe3atauSCixYsID333+fhIQE2rZtyyeffEKXLl1KPbdPnz4lpuIC3HvvvaxZs8b0ODIykilTprB582b0ej3h4eH8/PPPNGjQoErqLND2R0k9A4e+g9xULTm07eNa70ZAe21V0RqmlOLMpWw2RCaxOeYi+85cJvOqXowGHg6E+7vQJsiVNoFu+LjY4uYgG6EJIUR1q9VZKt9//z0REREsWrSIrl27Mn/+fAYMGEB0dDQ+PiUXiVq5ciX5+VeWh05JSaFt27Y8+uijprKTJ0/Ss2dPnn32Wd544w1cXFw4evSoDPNUpbxMSDkB5/fB6b+1oZQ+hUMprsHa6qI1JDO3gF2xl9gUncS2EymcSs4yO+5gY0m4vwvhAS50bOBGsKcD3k52shGaEELUsFoNOObNm8fzzz/P8OHDAVi0aBFr1qxhyZIlTJ06tcT5Hh4eZo9XrFiBg4ODWcAxffp07r33XubOnWsqk2XYq5DRqM1KuRwHe77Uyto/XbiKqAV4NqrwUIqVlRU+Pj5YWV3/46g3GDmdksX6yCS2HL/IwbhUsvLNdygO8bzSi9EywBUfZ1vZCE0IIWpZrQUc+fn57Nu3j2nTppnKLCws6N+/Pzt27CjXPRYvXszjjz+Oo6PWfW80GlmzZg2vvPIKAwYM4MCBA4SFhTFt2jQGDRpUHc249WRc0PI2Dn0Deeng2VibBpuTCgHtwNapQrfLydfTrEVLvIPC8HB2IDtfj4PNlY+lUoq0nAJ2ntJ6MXaeSuF0SrbZPRxtLAkPcKFlgAvtgt0JcrfH29nWlIshG6EJIUTtq7WAIzk5GYPBUGLXWV9fX6Kioq57/e7duzly5AiLFy82lSUlJZGZmcmcOXN4++23ee+991i7dq1pw7nevXuXeq+8vDzy8vJMjzMyMirZqnouL0OblXJuN8Tt0JJD+0zTgg3XQHAOqNjtCgws2nyKpdtjSc/R42JvxfDuYbzQpxEXUnNYeySBv48nc+hcKtlX9WKEejoQHuBC60A3wgNc8Ha2kY3QhBCiDqt0wHHixAlOnjxJr169sLe3RylVo7/kFy9eTOvWrc0STI1GIwAPPPAAEydOBKBdu3Zs376dRYsWXTPgmD17Nm+88Ub1V/pmZjRoeRupZ2FPYZDXYRg4eIJC6+mowFBKTr6eRZtP8fH646ay9Bw9H68/jlEpWge6MvePaNMxJ1srWvq7EB7gTLtgNwLcHfB2spGN0IQQ4iZR4YAjJSWFwYMHs2HDBnQ6HcePH6dhw4Y8++yzuLu78+GHH5brPl5eXlhaWpKYmGhWnpiYiJ9f2es3ZGVlsWLFCt58880S97SysiI8PNysvEWLFmzduvWa95s2bRoRERGmx+fPny9xj1te+nlIPQcH/qst6uXdDFo/CtnJ4N8WbJ0rdDtLCwuWbi99X57lO06zc1o/2ge7EeBmT+tAF8L9XfB0spWN0IQQ4iZV4T8LJ06ciJWVFXFxcTg4XFlFcvDgwaxdu7bc97GxsaFjx46sX7/eVGY0Glm/fj3dunUr89off/yRvLw8nnrqqRL37Ny5M9HR0WblMTExhISEXPN+tra2uLi4mH6cnSv25Vnv5aZD8kk4u1MbTrGwLhxKSQHXIG2jtgrKyC0gPUdf6rH0HD2ZuXrefKAlL9/VlEc7BdO1kSctA13xd7XHydZKgg0hhLjJVLiHY926dfzxxx8EBQWZlTdp0oQzZ85U6F4REREMGzaMTp060aVLF+bPn09WVpZp1srQoUMJDAxk9uzZZtctXryYQYMG4enpWeKekydPZvDgwfTq1Ys77riDtWvX8r///Y9NmzZVrKFCUzSUknYG9i7VyjqNAHt3bcaKR0OwqPj6Fc521rjYW5UadLjYW+HmYIOXk60sIS6EEPVEhQOOrKwss56NIpcuXcLW1rZC9xo8eDAXL15k5syZJCQk0K5dO9auXWtKJI2Li8PiqryA6Ohotm7dyrp160q954MPPsiiRYuYPXs248ePp1mzZvz888/07NmzQnUThdLOQdp52LccCrLAJxxaPQSZF8G/Ndi5Vuq2qTn5DOsWyicbTpQ4Nrx7GHqjEZtyTJMVQghxc9AppdT1T7vi3nvvpWPHjrz11ls4Oztz+PBhQkJCePzxxzEajfz000/VVdcac+7cOYKDgzl79myJnpxbSk4qnN8PJ9bBzoVgaQMPfwk6S21xL/+2YFnxoCApPZdx3+5n4VMdWbb9NMt3nDabpTKmTyNsZe8SIYSo8yryfVnhb4u5c+fSr18/9u7dS35+Pq+88gpHjx7l0qVLbNu2rdKVFnWMQV+4wNfpK0MpXZ4HOzcw6rUFvioRbOTpDYxYvocj59N54et9fDS4HS/2bUJGbgHOdtbojUYJNoQQoh6qcNJoq1atiImJoWfPnjzwwANkZWXx0EMPceDAAVnRsz5JO6sNp+xfBvpc8GsD4Q9oi315NAR7twrfUinF5B8Pc+R8OvbWljzaKQgnO2tsrCzwdLLFxsrCbNEvIYQQ9UeFfrsXFBRw9913s2jRIqZPn15ddRK1LecyXDoJZ7ZC/CGwsoM+UyArRRtKcQ2u1G0XbjrJ6kMX0OnghT4N6d3MB1d76yquvBBCiLqoQj0c1tbWHD58uLrqIuoCgx6ST8DlM7D/K62s6yiwdQFL60oPpfx1LJEP1mnTlR/pGMSgdoH4OMuGekIIcauo8JDKU089ZbacuKhnUuO0WSl7F2tDKQHtofl9WgKpRyNtOmwFHU/IYPyKAxgVdG/kyXM9wghyLznTSQghRP1V4T9V9Xo9S5Ys4a+//qJjx46mjdOKzJs3r8oqJ2pY9iVtKOX0Fkg8Ctb20PsVyLoEzn7gVvGhlLTsAoYv30N2voGGXo5E3NmUhj5Osr6GEELcYioccBw5coQOHToA2gqexcnqjzcxQwGkHNeGUg78Vyu7bQzYOIE+T9srxbJi+RZ6g5Hn/7uXc5dzcHewZtq9zWkZ4Cq7twohxC2owgHHxo0bq6MeorZdPgNpF2DPF2DIh6DO0PQeyIgH35bg4FHhW85cfZTdsZewsbJg0l3N6BLqib2NTHkVQohb0Q39qXnu3DnOnTtXVXURtSUrBS6dgthNcDEKrB2h12TIvgjOvpWalfLfHaf5dlccAM/1DOPOcF9cHWRGihBC3KoqHHAYjUbefPNNXF1dCQkJISQkBDc3N9566y3T9vDiJqLPLxxKiYUD32hl3ceBjaO2oqhHY7CyqdAtd55M4Y3/HQPg/rb+DO4cjK+LzEgRQohbWYWHVKZPn87ixYuZM2cOPXr0AGDr1q28/vrr5Obm8s4771R5JUU1unwa0i/Ani/BWAANukHjO7WhFO8W4Fhyg7yynL2Uxaiv96E3Kjo0cOOF3o0IlhkpQghxy6twwLF8+XK+/PJL7r//flNZmzZtCAwMZMyYMRJw3EyykrWejZMbIDkGbJ3h9knatvOO3uAeUrHb5ekZvmwvaTkFBLvbM/Xu5jTycZYZKUIIISo+pHLp0iWaN29eorx58+ZcunSpSiolaoA+D5ILh1IOfaeVdR8P1g6gAK8mFRpKMRoV477dz4mkTJztrJh6T3NaBbliYyUzUoQQQlQi4Gjbti2ffvppifJPP/2Utm3bVkmlRA24FAvp8bDrc20zttCe0KgvZKeAexg4elXodnP/iGJj9EWsLHRM7N+UHo29ZF8UIYQQJpXaLfa+++7jr7/+olu3bgDs2LGDs2fP8ttvv1V5BUU1yEzSejZOrNMW+rJ1gZ4RWrDh4AUeoRW63f8dOM+izacAGNYtlHtb++PmULFEUyGEEPVbhXs4evfuTXR0NA8++CCpqamkpqby0EMPER0dze23314ddRRVqSC3cK+U03D4e62s50SwtgNlBK/GYGVb7tsdOpvKKz9p++vcGe7D090a4OcqM1KEEEKYq1Sfd2BgoCSH3oyU0no2MuJh1yItwGh4BzTsre2f4tVESxYtp6T0XJ5bvod8g5GWAS5M6NeEYA/H618ohBDillPhHo6lS5fy448/lij/8ccfWb58eZVUSlSTzCS4dBpi1mo9HPbu0HOCNlvFwUPL3Sjn8vS5BQZGLNvDxcx8fF1smX5vc5r6umApM1KEEEKUosIBx+zZs/HyKplQ6OPjw7vvvlsllRLVoCCncIGvk3DkJ62sZwRY2oLRAJ5NtGGVclBK8fKPhzhyIR0HG0um3dOCtsHuMiNFCCHENVX4GyIuLo6wsLAS5SEhIcTFxVVJpUQVUwpSTkF6AuwsHEpp3F+bmZKVDG4h4ORT7tv9Z9NJfj0cj4UOXryjMb2aeuNoKzNShBBCXFuFAw4fHx8OHz5covzQoUN4elZsVUpRQzITIfUMRK+BtLPg4KmtuZGdog2reDYs91DKX8cS+eCPaAAe79yAQR0C8XCUGSlCCCHKVuGA44knnmD8+PFs3LgRg8GAwWBgw4YNTJgwgccff7w66ihuRH62tsBXygk4ukor6/WytqiXoUBLFLW2L9etYhLSGb/iAAro2diLZ3uG4id7pAghhCiHCveDv/XWW5w+fZp+/fphZaVdbjQaGTp0qORw1DVKabvAZibAroWA0racD74N0s6BR0Nw8i3XrS5n5TN82V6y8w009nbklQFNCfVyQlfOnhEhhBC3tgoHHDY2Nnz//fe8/fbbHDx4EHt7e1q3bk1ISMX23RA1ICNeG0qJ/J+2QZujN3QbAzmXwM5NCzjKETAUGIyM+u8+zqfm4Olow4x/hdPMX2akCCGEKL9KZ/o1adKEJk2aYDAY+Oeff3BxccHd3b0q6yZuRH6WNpSSfByO/aKV9ZoMljaQlwEB4WBTvl1cZ/1yhN2nL2FrZcHUu5vTMdQDWyvLaqy8EEKI+qbCORwvvfQSixcvBsBgMNC7d286dOhAcHAwmzZtqur6icowGrVZKRmJsHOhVtZ8IAR1hsyL4NoAnPzKdavl20/z7e6z6IDRvRvSt4UPTjIjRQghRAVVOOD46aefTJu0/e9//+PUqVNERUUxceJEpk+fXqlKLFiwgNDQUOzs7OjatSu7d+++5rl9+vRBp9OV+LnvvvtKPX/06NHodDrmz59fqbrdlExDKf+n5W84+cJtLxQOpbhos1Isrv/Wbz+RzJu/HgNgULtABndugKdT+Zc9F0IIIYpUOOBITk7Gz0/76/i3337jscceo2nTpowYMYJ//vmnwhX4/vvviYiIYNasWezfv5+2bdsyYMAAkpKSSj1/5cqVxMfHm36OHDmCpaUljz76aIlzV61axc6dOwkICKhwvW5aeZmFQynRELVGK+s9BSwstX1UPBuDzfWXH49LyWb01/swGBWdQ90Ze0cj/GWPFCGEEJVU4YDD19eXY8eOYTAYWLt2LXfeeScA2dnZWFpWfFx/3rx5PP/88wwfPpzw8HAWLVqEg4MDS5YsKfV8Dw8P/Pz8TD9//vknDg4OJQKO8+fP8+KLL/LNN99gbW1d4XrdlIxGSDmpLWG+c5FWFj4IAtprC3y5BoOz/3Vvk5FbwDPLdpOeq6eBhwNT72lOmLfMSBFCCFF5FR6MHz58OI899hj+/v7odDr69+8PwK5du2jevHmF7pWfn8++ffuYNm2aqczCwoL+/fuzY8eOct1j8eLFPP744zg6Xvmr3Wg08vTTTzN58mRatmx53Xvk5eWRl5dnepyRkVGBVtQh6ee1hb2O/gRZSeAcAF1HQW4q2DiBZ6PrDqUYjIpx3x7g1MUsXOyseO2+5rQMcJUZKUIIIW5IhQOO119/nVatWnH27FkeffRRbG21MX1LS0umTp1aoXslJydjMBjw9TVfC8LX15eoqKjrXr97926OHDliSmIt8t5772FlZcX48ePLVY/Zs2fzxhtvlL/idVFehra418VIiPkD0EGfqdpQSn42BLQDW6fr3mbO75FsjrmIlYWOl+9qRrdGXthZy4wUIYQQN6ZS0w0eeeQRAM6dO4fRaMTCwoJhw4ZVacXKY/HixbRu3ZouXbqYyvbt28fHH3/M/v37yz0EMG3aNCIiIkyPz58/T3h4eJXXt9oYDVqwUXwopfUj4N9G23beNVDr7biOlfvP8cXfsQA82zOMe9v442x3iwxHCSGEqFY3tL1neHg4p0+frvT1Xl5eWFpakpiYaFaemJhoSky9lqysLFasWMGzzz5rVv7333+TlJREgwYNsLKywsrKijNnzjBp0iRCQ0NLvZetrS0uLi6mH2dn50q3qVakn9dWDv3nB8guzNXo/BzkpIK1o5Yoep2hlANxl5nys7ZHzt0t/Xi6WwheMiNFCCFEFbmhgEMpdUNPbmNjQ8eOHVm/fr2pzGg0sn79erp161bmtT/++CN5eXk89dRTZuVPP/00hw8f5uDBg6afgIAAJk+ezB9//HFD9a2TctMg+QQkHoUTf4HOQhtK0VlAfiZ4NQLbsgOohLRcnv9qLwUGRetAFyLuakKgW/n2VxFCCCHKo9ZXcIqIiGDYsGF06tSJLl26MH/+fLKyshg+fDgAQ4cOJTAwkNmzZ5tdt3jxYgYNGlRih1pPT88SZdbW1vj5+dGsWbPqbUxNMxq0YCMrEXYVDqW0GQy+LbVeD9cgcAks8xa5BQZGLNtDcmY+fq52zPhXOA1ljxQhhBBV7IYCjldffRUPD48bqsDgwYO5ePEiM2fOJCEhgXbt2rF27VpTImlcXBwWVw0HREdHs3XrVtatW3dDz33TS43T9kg5uAJyLoN7KHR8RpuVYuWg7ZVice2ET6UUET8c5Fh8Oo42lsy4rwVtgtywsryhji8hhBCiBJ260XGReujcuXMEBwdz9uxZgoKCars6pctJhfP7IG4nbJmrDaE88B/wagzpCeDfWgtAyvDv9ceZ92cMFjqYek8LHu8SjIskiQohhCininxfVtmfsmfPnmXEiBFVdTtRFoO+cFZKIuz+TCtrNwR8mmv7p7gEgEvZb/y6owl89GcMAE91bcCD7QMl2BBCCFFtqizguHTpEsuXL6+q24mypJ0tHEr5Rksa9WgEHYZBbjpY22sLfFlee7QsKiGdCSsOooDeTb15vldDvJ1lRooQQojqU+4cjtWrV5d5/NSpUzdcGVEOOZe13o34Q3B6K+gsC2el6CAvHXxbgb3bNS+/lJXP8KV7yCkw0NTXiSkDmhHkXr5t6oUQQojKKnfAMWjQIHQ6XZlTYWVmQzUzFGizUjKTrgyldBgKXk0gPV7bJ8U1+JqX5+uNjPxqL/FpuXg52TDzX+E08XOW900IIUS1K/eQir+/PytXrsRoNJb6s3///uqspwBILRxKOfBfbSlzr6bQ/knt35bWZQ6lKKWY8csR9p65jK2VBa/e24JOoR5Yy4wUIYQQNaDc3zYdO3Zk37591zx+vd4PcYOyL8Glk3BhP8TtAAsrbSgFtBkrHo3A3v2aly/bfprv95xFB4y7ozH9WvjKHilCCCFqTLmHVCZPnkxWVtY1jzdu3JiNGzdWSaXEVfT5kHJcm5Wy5wutrONwbZ2N9ARw9gO3aw+lbD1+kbd+PQbAwx0DGdw5GFd7mZEihBCi5pQ74Lj99tvLPO7o6Ejv3r1vuEKiFJfPaIHFvmWQnwXeLaDt4MKhFCttrxTL0gOI08lZvPDNfowKuoZ58OIdjfFxsavZ+gshhLjllXtI5dSpUzJkUhuyUuByLJzfC+f2aIFFn6mArnAopSE4lL7aa3puAcOX7SEjV0+opwMz7gsn2MOxRqsvhBBCQAUCjiZNmnDx4kXT48GDB5fY5VVUMX0+JB+HzATY86VW1ulZcA+BrCRw9r3mrBSDUTHum/3EJmfham/NrIEtaebvjIWFzEgRQghR88odcFzdu/Hbb7+VmdMhqsDl01qwsXcJFGRra2y0flQbVtFZgkdjsLIp9dLZv0Wy5Xgy1pY6ptzdjNsaesqMFCGEELVGvoHqqsyLcOkUnN0NFw6Ape2VWSnZl8C9ITh6lnrpT3vP8uXWWABG9mrIfa0DsLeRGSlCCCFqT7kDDp1OV2KBKFkwqpro87TVRLMSYe9irazrSG27+exkcPTWhlVKsT/uMtNW/QPAv9r4M7RbKK4OMiNFCCFE7Sr3LBWlFM888wy2ttqeG7m5uYwePRpHR/MkxJUrV1ZtDW81SsGl2MKN2b4EfS74t4WWD0J+Nii0lUVLGUq5kJrDc8v3UmBQtAt2ZdJdTfGVGSlCCCHqgHIHHMOGDTN7/NRTT1V5ZQSQdVGblXJmGyQcBis76D1FO5adAl7NwNGrxGU5+QaeXb6HS1n5BLrZMetfLQmRGSlCCCHqiHIHHEuXLq3OegiAglxIjoGMBNhXuPPubS9o281nJoGDF3iElrhMKUXEDweJjM/A0daSmf8Kp2Wgq8xIEUIIUWdI0mhdoZTWs5GZDLs/B0MeBHaEFvdrM1SUEbwag1XJbeQ//us4vx9JwNJCx8t3NeP2pt7YWMlbK4QQou6Qb6W6IjNRy904vQWSjoG1A/R+BVDa4l/uoVqy6FV+/yee+euPAzC0WwiD2gXiYFPujishhBCiRkjAURcU5BQu8BUPB77SyrqNBSdfyErWVhJ1D4OrZgUdu5DOxB8OAtCvuQ+jejXC3bH0dTmEEEKI2iQBR21TClJOaoHFrs/BUADBXaHZvVogYjSAZxOwNp9tkpyZx4jle8gtMNLcz5lp9zTHz1VmpAghhKibJOCobRkJkBoHsZvgYhTYOEKvl7VjWcngFgJOPmaX5OuNjPxqLwlpuXg72/LGAy0J83aq+boLIYQQ5SQBR23Kz9aGUjLi4cDXWln38VquRnYK2LuDZ0OzoRSlFK+u+of9canYW1sw474WtAt2w1JmpAghhKjDJOCoLUajNpSSkwI7F4JRDw26Q5O7tMW+DAXaAl/W9maXLdkay0/7zqHTwfh+TenXwhdbK1m2XAghRN0mAUdtyYiH1DNwYj2kHAdbF+g1STuWeRHcGmhJo8VsibnIO79FAvB4p2AGdw7G0VZmpAghhKj7JOCoDXmZ2l4pGRfg4LdaWY8J4OAJOZfAzg08zIdSTl3MZOy3+zEq6N7Ikxf7NcZDZqQIIYS4SdSJgGPBggWEhoZiZ2dH165d2b179zXP7dOnj2kjueI/9913HwAFBQVMmTKF1q1b4+joSEBAAEOHDuXChQs11ZyyFQ2lZBUOpSgDhPWCRn21Tdv0edoCXzYOpkvScgoYvmwPGbl6Gno5MuO+cPxd7ct4EiGEEKJuqfWA4/vvvyciIoJZs2axf/9+2rZty4ABA0hKSir1/JUrVxIfH2/6OXLkCJaWljz66KMAZGdns3//fmbMmMH+/ftZuXIl0dHR3H///TXZrGvLuADp5+DEOm37eTtX6DlRO5aZBK4NwMnPdLreYGTsN/s5k5KNu4M1bw1qSVM/Z9mpVwghxE2l1hMA5s2bx/PPP8/w4cMBWLRoEWvWrGHJkiVMnTq1xPkeHh5mj1esWIGDg4Mp4HB1deXPP/80O+fTTz+lS5cuxMXF0aBBg2pqSTnkZUDyCUg7B4e/18p6RmizUbJTtODDsyFYXIkD3/ktkq0nkrGxtGDavS3oFOohM1KEEELcdGq1hyM/P599+/bRv39/U5mFhQX9+/dnx44d5brH4sWLefzxx3F0vPbOqGlpaeh0Otzc3Eo9npeXR3p6uuknIyOjQu0ol+KzUnYs0PZGadQXGvbWhlEKcsGzsbYOR6Hv98SxdNtpAMb0acS9rf1lRooQQoibUq0GHMnJyRgMBnx9zWdj+Pr6kpCQcN3rd+/ezZEjR3juueeueU5ubi5TpkzhiSeewMXFpdRzZs+ejaurq+knPDy8Yg0pj4Jsbd2N2C3a7BR7dy1RVCltVoprMDj7m07fe/oS01cdAeCBdgE83S0EJ5mRIoQQ4iZV6zkcN2Lx4sW0bt2aLl26lHq8oKCAxx57DKUUCxcuvOZ9pk2bRlpamunn2LFjVVvR/GywstG2lh/wLjz+Ddz1jjaEkpsKts7g2cg0lHI+NYeR/92H3qjo0MCNlwc0w9Op5C6xQgghxM2iVv9k9vLywtLSksTERLPyxMRE/Pz8rnGVJisrixUrVvDmm2+Werwo2Dhz5gwbNmy4Zu8GgK2tLba2V77Q09PTK9CK69Dnwrb5sOszLbiwc4MuI7VE0ZTjWjAS0A5staXJs/P1jFi2h0tZ+QS52/PGAy0JcpMZKUIIIW5utdrDYWNjQ8eOHVm/fr2pzGg0sn79erp161bmtT/++CN5eXk89dRTJY4VBRvHjx/nr7/+wtPTs8rrXi752fD3PNj8nhZsgPbfLXO1IMTODVwDwTkAAKNRMfH7g0QnZOBsa8Ub97ekhZ+LzEgRQghx06v1IZWIiAi++OILli9fTmRkJC+88AJZWVmmWStDhw5l2rRpJa5bvHgxgwYNKhFMFBQU8Mgjj7B3716++eYbDAYDCQkJJCQkkJ+fXyNtMrG00no2SrPrM3D203aCLRxKmf9XDH8cTcTSQscrdzejR2MvrCxr/S0SQgghblitZyEOHjyYixcvMnPmTBISEmjXrh1r1641JZLGxcVhYWH+pRsdHc3WrVtZt25difudP3+e1atXA9CuXTuzYxs3bqRPnz7V0o5S5aZd6dkocSwVctPByRuANYfj+feGEwA82zOMQe0DsbOWGSlCCCHqB51SStV2Jeqac+fOERwczNmzZwkKCqr8jQz58H6T0oMOOzeYfBwsbThyPo2HF24nT29kQLgv7zzUGi9JEhVCCFHHVeT7Uvrrq5NBD11HlX6s6ygw6LmYkcdzy/eSpzcS7u/CtPuaS7AhhBCi3qn1IZV6zcYBbo/Q/l18lkrXUXB7BPlY8/xXO0lIz8XXxZZ3BrUixOPaC5gJIYQQNysJOKqblR30eAljz0noM5OxcvLEwqhHWdoy7cdDHDybir21JbMGhtM6yFVmpAghhKiXZEilJtg4cOTwQb788guOHNgPtk58+fcpft5/Hp0OIu5sQt/mvjIjRQghRL0l33A1RK+zJilDj97Kjo3RScz+PQqAJ7uGMLhLA5mRIoQQol6TIZUaYmVlhY9fAEk5ikm/7Meo4PYmXrzYtxEudta1XT0hhBCiWknAUQNy8vU0a9ES76CGuDrZ8ZFbMit2n+W1fzXH10WWLRdCCFH/ScBRzfIKDCzafIql22NJz9HjYm/FsG6hfPpke+xkq3khhBC3CAk4qlFOvp5Fm0/x8frjprL0HD2fbDiBhU7HqN4NcbCRt0AIIUT9J0mj1cjSwoKl22NLPbZ0eyxWFvLyCyGEuDXIN141ysgtID1HX+qx9Bw9GbkFNVwjIYQQonZIwFGNnO2scbEvfcjExd4KZ5mdIoQQ4hYhAUc1MhiNDO8eVuqx4d3D0BuNNVwjIYQQonZIxmI1srexYkyfRgBms1SGdw9jTJ9G2MpiX0IIIW4REnBUM1trS0b1bsjYOxqTkVuAs501eqNRgg0hhBC3FAk4akDR1FfPwm3nbWQkSwghxC1GvvmEEEIIUe0k4BBCCCFEtZOAQwghhBDVTgIOIYQQQlQ7SRothbFwfYz4+PharokQQghRdxV9TxrLsa6UBBylSExMBKBLly61XBMhhBCi7ktMTKRBgwZlnqNTSqkaqs9NQ6/Xc+DAAXx9fbGo5AZrGRkZhIeHc+zYMZydnau4hnWftF/af6u2/1ZuO0j7b7X2G41GEhMTad++PVZWZfdhSMBRTdLT03F1dSUtLQ0XF5fark6Nk/ZL+2/V9t/KbQdp/63e/rJI0qgQQgghqp0EHEIIIYSodhJwVBNbW1tmzZqFra1tbVelVkj7pf23avtv5baDtP9Wb39ZJIdDCCGEENVOejiEEEIIUe0k4BBCCCFEtZOAQwghhBDVTgKOSpo9ezadO3fG2dkZHx8fBg0aRHR0tNk5oaGh6HQ6VqxYUeL6li1botPpWLZsWQ3VuPrMmTMHnU7HSy+9ZCqr720/f/48Tz31FJ6entjb29O6dWv27t1rOt6nTx90Oh1z5swpce19992HTqfj9ddfr8EaVx2DwcCMGTMICwvD3t6eRo0a8dZbb1E8Haw+tX/Lli0MHDiQgIAAdDod//d//2d2XCnFzJkz8ff3x97env79+3P8+HGzc3Q6HTqdjp07d5qV5+Xl4enpiU6nY9OmTdXcksopq/0FBQVMmTKF1q1b4+joSEBAAEOHDuXChQtm97hZ23+997640aNHo9PpmD9/vln5zdr26iABRyVt3ryZsWPHsnPnTv78808KCgq46667yMrKMjsvODiYpUuXmpXt3LmThIQEHB0da7LK1WLPnj189tlntGnTpsSx+tr2y5cv06NHD6ytrfn99985duwYH374Ie7u7mbnBQcHlwiqzp8/z/r16/H396/BGlet9957j4ULF/Lpp58SGRnJe++9x9y5c/nkk0/Mzqsv7c/KyqJt27YsWLCg1ONz587l3//+N4sWLWLXrl04OjoyYMAAcnNzzc4r7f+HVatW4eTkVG11rwpltT87O5v9+/czY8YM9u/fz8qVK4mOjub+++8vce7N2P7rvfdFVq1axc6dOwkICCj1+M3Y9mqhRJVISkpSgNq8ebOpLCQkRE2dOlXZ2tqquLg4U/nzzz+vXnzxReXq6qqWLl1aC7WtGhkZGapJkybqzz//VL1791YTJkwwHavPbZ8yZYrq2bNnmef07t1bvfDCC8rT01Nt3brVVP7OO++ogQMHqrZt26pZs2ZVc02rx3333adGjBhhVvbQQw+pJ5980vS4vrYfUKtWrTI9NhqNys/PT73//vumstTUVGVra6u+++47s+tee+015eLiorKzs03ld955p5oxY4YC1MaNG2uiCTfk6vaXZvfu3QpQZ86cMbvuZm//tdp+7tw5FRgYqI4cOaJCQkLURx99VOK6m73tVUV6OKpIWloaAB4eHmblvr6+DBgwgOXLlwPaXwTff/89I0aMqPE6VrWxY8dy33330b9//1KP19e2r169mk6dOvHoo4/i4+ND+/bt+eKLL0qcZ2Njw5NPPmn2l82yZctu+vZ3796d9evXExMTA8ChQ4fYunUr99xzj9l59bX9xcXGxpKQkGD2/4Crqytdu3Zlx44dZud27NiR0NBQfv75ZwDi4uLYsmULTz/9dI3WubqlpaWh0+lwc3MzK6+P7TcajTz99NNMnjyZli1bXvO8+tj2ypCAowoYjUZeeuklevToQatWrUocHzFiBMuWLUMpxU8//USjRo1o165dzVe0Cq1YsYL9+/cze/bsMs+rj20/deoUCxcupEmTJvzxxx+88MILjB8/3hRYFTdixAh++OEHsrKy2LJlC2lpafzrX/+qhVpXnalTp/L444/TvHlzrK2tad++PS+99BJPPvlkiXPrY/uLS0hIALTgujhfX1/TseJGjBjBkiVLAC34uvfee/H29q7+itaQ3NxcpkyZwhNPPFHqPiL1rf3vvfceVlZWjB8//rrn1re2V4YEHFVg7NixHDlypNQESdCS5DIzM9myZQtLliy56f/CO3v2LBMmTOCbb77Bzs6uzHPrW9tBCzA7dOjAu+++S/v27Rk5ciTPP/88ixYtKnFu27ZtadKkCT/99BNLlizh6aefvu6OinXdDz/8wDfffMO3337L/v37Wb58OR988EGpAVd9bP+NeOqpp9ixYwenTp2qd709BQUFPPbYYyilWLhwYann1Kf279u3j48//phly5ah0+mue359antl3br/51eRcePG8euvv7JlyxaCgoJKPcfKyoqnn36aWbNmsWvXLlatWlXDtaxa+/btIykpiQ4dOpjKDAYDW7Zs4dNPPyUvL89UXt/aDuDv7094eLhZWYsWLUzdpVcbMWIECxYs4NixY+zevbsmqlitJk+ebOrlAGjdujVnzpxh9uzZDBs2rMT59a39xfn5+QGQmJholgibmJhYak+ep6cn//rXv3j22WfJzc3lnnvuISMjo6aqW22Kgo0zZ86wYcOGa+6SWp/a//fff5OUlESDBg1MZQaDgUmTJjF//nxOnz5tdn59antlSQ9HJSmlGDduHKtWrWLDhg2EhYWVef6IESPYvHkzDzzwQInZDDebfv368c8//3Dw4EHTT6dOnXjyySc5ePAglpaWZufXp7YD9OjRo8QU6JiYGEJCQko9f8iQIfzzzz+0atWqRKByM8rOzsbCwvxXh6WlJUajsdTz61v7iwsLC8PPz4/169ebytLT09m1axfdunUr9ZoRI0awadMmhg4dWuL/lZtRUbBx/Phx/vrrLzw9Pcs8v760/+mnn+bw4cNmvwcDAgKYPHkyf/zxR6nX1Je2V5b0cFTS2LFj+fbbb/nll19wdnY2jde6urpib29f4vwWLVqQnJyMg4NDTVe1yjk7O5fIVXF0dMTT07PUHJb61HaAiRMn0r17d959910ee+wxdu/ezeeff87nn39e6vnu7u7Ex8djbW1dwzWtHgMHDuSdd96hQYMGtGzZkgMHDjBv3rxrdhHf7O3PzMzkxIkTpsexsbEcPHgQDw8PGjRowEsvvcTbb79NkyZNCAsLY8aMGQQEBDBo0KBS73f33Xdz8eLFa/YC1DVltd/f359HHnmE/fv38+uvv2IwGEy/Cz08PLCxsSlxv5up/dd7768OrqytrfHz86NZs2al3u9mant1kICjkorGKPv06WNWvnTpUp555plSr7le5F+f1ae2d+7cmVWrVjFt2jTefPNNwsLCmD9/fqlJk0Wuzti/mX3yySfMmDGDMWPGkJSUREBAAKNGjWLmzJnXvOZmbv/evXu54447TI8jIiIAGDZsGMuWLeOVV14hKyuLkSNHkpqaSs+ePVm7du0185t0Oh1eXl41UveqUFb7X3/9dVavXg1QYghp48aNJX4/ws3V/uu99xV1M7W9OshusUIIIYSodpLDIYQQQohqJwGHEEIIIaqdBBxCCCGEqHYScAghhBCi2knAIYQQQohqJwGHEEIIIaqdBBxCCCGEqHYScAghhBCi2knAIW55ffr04aWXXqrtapgopRg5ciQeHh7odDoOHjxY7c/5+uuvl7rZWFlCQ0OZP39+tdTnZlAT7V+/fj0tWrTAYDAAlXufqopOp+P//u//ADh9+rTZZ3PTpk3odDpSU1MBbfv1mlxdNjk5GR8fH86dO1djzykqTgIOIeqYtWvXsmzZMn799Vfi4+NL3Z+mqn+hv/zyy2YbkJXHnj17GDlyZJXVQZT0yiuv8Nprr5k2+qrM+1RV4uPjueeee8p17uDBg4mJianmGl3h5eXF0KFDmTVrVo09p6g4CTiEqAYGg+Gau6dez8mTJ/H396d79+74+flhZVX5LY/y8/PLdZ6Tk1OF97vx9vauNxvy1UVbt27l5MmTPPzww6ayyrxPVcXPzw9bW9tynWtvb4+Pj08118jc8OHD+eabb7h06VKNPq8oPwk4RJ3Qp08fxo8fzyuvvIKHhwd+fn68/vrrpuNXd+ECpKamotPp2LRpE3ClW/ePP/6gffv22Nvb07dvX5KSkvj9999p0aIFLi4uDBkyhOzsbLPn1+v1jBs3DldXV7y8vJgxYwbFtxnKy8vj5ZdfJjAwEEdHR7p27Wp6XrjS47B69WrCw8OxtbUlLi6u1LZu3ryZLl26YGtri7+/P1OnTkWv1wPwzDPP8OKLLxIXF4dOpyM0NLTE9Zs2bWL48OGkpaWh0+nQ6XSm1yo0NJS33nqLoUOH4uLiYuqBmDJlCk2bNsXBwYGGDRsyY8YMCgoKTPe8uqv+mWeeYdCgQXzwwQf4+/vj6enJ2LFjza65ekhBp9Px5Zdf8uCDD+Lg4ECTJk1MG3sVWb16NU2aNMHOzo477riD5cuXm3XFX00pxeuvv06DBg2wtbUlICCA8ePHm47/97//pVOnTjg7O+Pn58eQIUNISkoye60q85no06cP48aNK/MzcbXU1FSee+45vL29cXFxoW/fvhw6dMh0/NChQ9xxxx04Ozvj4uJCx44d2bt37zXvt2LFCu68806zTeAq8z5dregeS5YsoUGDBjg5OTFmzBgMBgNz587Fz88PHx8f3nnnHbPrig+pXE9pPXALFy6kUaNG2NjY0KxZM/773/+WuH9Zn5/Lly/z5JNP4u3tjb29PU2aNGHp0qWm4y1btiQgIIBVq1aVq46iFigh6oDevXsrFxcX9frrr6uYmBi1fPlypdPp1Lp165RSSsXGxipAHThwwHTN5cuXFaA2btyolFJq48aNClC33Xab2rp1q9q/f79q3Lix6t27t7rrrrvU/v371ZYtW5Snp6eaM2eO2XM7OTmpCRMmqKioKPX1118rBwcH9fnnn5vOee6551T37t3Vli1b1IkTJ9T777+vbG1tVUxMjFJKqaVLlypra2vVvXt3tW3bNhUVFaWysrJKtPPcuXPKwcFBjRkzRkVGRqpVq1YpLy8vNWvWLKWUUqmpqerNN99UQUFBKj4+XiUlJZW4R15enpo/f75ycXFR8fHxKj4+XmVkZCillAoJCVEuLi7qgw8+UCdOnFAnTpxQSin11ltvqW3btqnY2Fi1evVq5evrq9577z3TPWfNmqXatm1rejxs2DDl4uKiRo8erSIjI9X//ve/Eq9JSEiI+uijj0yPARUUFKS+/fZbdfz4cTV+/Hjl5OSkUlJSlFJKnTp1SllbW6uXX35ZRUVFqe+++04FBgYqQF2+fLnUz8WPP/6oXFxc1G+//abOnDmjdu3aZVaHxYsXq99++02dPHlS7dixQ3Xr1k3dc889puPV+Zm4uv39+/dXAwcOVHv27FExMTFq0qRJytPT09T+li1bqqeeekpFRkaqmJgY9cMPP6iDBw+W2m6llGrTpo1ZnSr7Pl1t1qxZysnJST3yyCPq6NGjavXq1crGxkYNGDBAvfjiiyoqKkotWbJEAWrnzp2m6wC1atUqpVTJ/x+LXuei93Hp0qXK1dXVdO3KlSuVtbW1WrBggYqOjlYffvihsrS0VBs2bDC7f1mfn7Fjx6p27dqpPXv2qNjYWPXnn3+q1atXm7Vt8ODBatiwYddsu6hdEnCIOqF3796qZ8+eZmWdO3dWU6ZMUUpVLOD466+/TOfMnj1bAerkyZOmslGjRqkBAwaYPXeLFi2U0Wg0lU2ZMkW1aNFCKaXUmTNnlKWlpTp//rxZ/fr166emTZumlNJ+wQJlfoEopdSrr76qmjVrZvZcCxYsUE5OTspgMCillProo49USEhImfe5+hd6kZCQEDVo0KAyr1VKqffff1917NjR9Li0L7KQkBCl1+tNZY8++qgaPHiw2XNdHXC89tprpseZmZkKUL///rtSSntNW7VqZVaP6dOnlxlwfPjhh6pp06YqPz//um1SSqk9e/YowBSAVddn4ur2//3338rFxUXl5uaa1adRo0bqs88+U0op5ezsrJYtW1audiillKurq/rqq6/MyirzPl1t1qxZysHBQaWnp5vKBgwYoEJDQ02fQaWUatasmZo9e7bp8Y0EHN27d1fPP/+8WT0effRRde+995rdv6zPz8CBA9Xw4cOv2S6llJo4caLq06dPmeeI2iNDKqLOaNOmjdljf39/s+7xytzH19fXNIxQvOzq+952223odDrT427dunH8+HEMBgP//PMPBoOBpk2b4uTkZPrZvHkzJ0+eNF1jY2NTog1Xi4yMpFu3bmbP1aNHDzIzM6ssw75Tp04lyr7//nt69OiBn58fTk5OvPbaa9cc8inSsmVLU7IilO/9KN5+R0dHXFxcTNdER0fTuXNns/O7dOlS5v0effRRcnJyaNiwIc8//zyrVq0yDT8B7Nu3j4EDB9KgQQOcnZ3p3bs3QIm2VfVn4mqHDh0iMzMTT09Ps89IbGys6TMSERHBc889R//+/ZkzZ47ZZ6c0OTk5ZsMp11KZ9yk0NBRnZ2fTY19fX8LDw7GwsDArq8z/f6WJjIykR48eZmU9evQgMjLSrKysz88LL7zAihUraNeuHa+88grbt28v8Tz29vYlhktF3SEBh6gzrK2tzR7rdDpT4mXRL0JVbAz9WuPUxe+j0+nKvG95ZGZmYmlpyb59+zh48KDpJzIyko8//th0nr29vdkXVG1xdHQ0e7xjxw6efPJJ7r33Xn799VcOHDjA9OnTr5tQWpnX7UZf66sFBwcTHR3Nf/7zH+zt7RkzZgy9evWioKCArKwsBgwYgIuLC9988w179uwxjd9f3baq/kxcLTMzE39/f7PPx8GDB4mOjmby5MmAljtx9OhR7rvvPjZs2EB4eHiZ+QZeXl5cvnz5us9dVe9TVb8mlVFWHe655x7OnDnDxIkTuXDhAv369ePll182O//SpUt4e3vXWH1FxVQ+/V2IGlT0SyQ+Pp727dsDVOn6FLt27TJ7vHPnTpo0aYKlpSXt27fHYDCQlJTE7bfffkPP06JFC37++WeUUqbgZNu2bTg7OxMUFFTu+9jY2JT6l3Zptm/fTkhICNOnTzeVnTlzpmIVrwLNmjXjt99+Myvbs2fPda+zt7dn4MCBDBw4kLFjx9K8eXP++ecflFKkpKQwZ84cgoODAcpMwqyosj4TV+vQoQMJCQlYWVmVmuhbpGnTpjRt2pSJEyfyxBNPsHTpUh588MFSz23fvj3Hjh27oTbUFS1atGDbtm0MGzbMVLZt2zbCw8MrdB9vb2+GDRvGsGHDuP3225k8eTIffPCB6fiRI0fo06dPVVVbVDHp4RA3BXt7e2677TbmzJlDZGQkmzdv5rXXXquy+8fFxREREUF0dDTfffcdn3zyCRMmTAC0L4knn3ySoUOHsnLlSmJjY9m9ezezZ89mzZo1FXqeMWPGcPbsWV588UWioqL45ZdfmDVrFhEREWbd2dcTGhpKZmYm69evJzk5ucxu5CZNmhAXF8eKFSs4efIk//73v2slk3/UqFFERUUxZcoUYmJi+OGHH1i2bBnANXuGli1bxuLFizly5AinTp3i66+/xt7enpCQEBo0aICNjQ2ffPIJp06dYvXq1bz11ltVVt+yPhNX69+/P926dWPQoEGsW7eO06dPs337dqZPn87evXvJyclh3LhxbNq0iTNnzrBt2zb27NlDixYtrvn8AwYMYOvWrVXWnto0efJkli1bxsKFCzl+/Djz5s1j5cqVJXooyjJz5kx++eUXTpw4wdGjR/n111/NXr/s7Gz27dvHXXfdVR1NEFVAAg5x01iyZAl6vZ6OHTvy0ksv8fbbb1fZvYcOHUpOTg5dunRh7NixTJgwwWxRq6VLlzJ06FAmTZpEs2bNGDRoEHv27KFBgwYVep7AwEB+++03du/eTdu2bRk9ejTPPvtshYOn7t27M3r0aAYPHoy3tzdz58695rn3338/EydOZNy4cbRr147t27czY8aMCj1fVQgLC+Onn35i5cqVtGnThoULF5p6Xa61voObmxtffPEFPXr0oE2bNvz111/873//w9PTE29vb5YtW8aPP/5IeHg4c+bMMftr90Zd7zNRnE6n47fffqNXr14MHz6cpk2b8vjjj3PmzBl8fX2xtLQkJSWFoUOH0rRpUx577DHuuece3njjjWs+/5NPPsnRo0eJjo6usjbVlkGDBvHxxx/zwQcf0LJlSz777DOWLl1aod4IGxsbpk2bRps2bejVqxeWlpasWLHCdPyXX36hQYMGN9wLKaqPTqkyJpYLIUQ1euedd1i0aBFnz56t7aqY6dOnD+3atav1pdsnT55Meno6n332Wa3W42Zw2223MX78eIYMGVLbVRHXID0cQoga85///Ic9e/Zw6tQp/vvf//L++++bjesLc9OnTyckJKTGkzdvNsnJyTz00EM88cQTtV0VUQbp4RBC1JiJEyfy/fffc+nSJRo0aMDTTz/NtGnTbmj59upQV3o4hKhPJOAQQgghRLWTIRUhhBBCVDsJOIQQQghR7STgEEIIIUS1k4BDCCGEENVOAg4hhBBCVDsJOIQQQghR7STgEEIIIUS1k4BDCCGEENVOAg4hhBBCVLv/B6J0uOmi20RUAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 550x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mean_linear = res_df[res_df.model == 'linear'].groupby('n_training_samples')['f1_score'].mean().to_numpy()\n",
    "mean_tabnet = res_df[res_df.model == 'tabnet'].groupby('n_training_samples')['f1_score'].mean().to_numpy() \n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(5.5, 3.5))\n",
    "sns.lineplot(\n",
    "    x='n_training_samples',\n",
    "    y='f1_score',\n",
    "    hue='model',\n",
    "    marker='o',\n",
    "    data=res_df.assign(model=lambda x: x.model.replace({'tabnet': 'scTab', 'Linear': 'linear'})),\n",
    ")\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "ax.set_xlabel('number of training samples (in millions)')\n",
    "ax.set_ylabel('F1-score (macro avg.)')\n",
    "for i in range(0, len(mean_linear)):\n",
    "    ax.arrow(\n",
    "        x=np.unique(res_df.n_training_samples)[i],\n",
    "        y=mean_linear[i],\n",
    "        dx=0.,\n",
    "        dy=(mean_tabnet - mean_linear)[i],\n",
    "        length_includes_head=True,\n",
    "        alpha=0.5\n",
    "    )\n",
    "ax.legend(title='')\n",
    "\n",
    "\n",
    "ax2 = ax.twiny()\n",
    "ax2.set_xlim(ax.get_xlim())\n",
    "ax2.set_xticks(n_train_samples)\n",
    "ax2.set_xticklabels(n_donors)\n",
    "ax2.set_xlabel('number of unique donors')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('/dss/dsshome1/04/di93zer/git/cellnet/figure-plots/figure2/linear_vs_tabnet.pdf')\n",
    "plt.savefig('/dss/dsshome1/04/di93zer/git/cellnet/figure-plots/figure2/linear_vs_tabnet.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42ca5086-d1c5-42a0-9aeb-e1ffbbd24aba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "929992d4-0c14-47ff-9467-75ff94b643ab",
   "metadata": {
    "tags": []
   },
   "source": [
    "# F1-score vs model size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "00815e48-45df-4d6e-95d2-11c91375859d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CKPTS_MODEL_SCALING = {\n",
    "    16: get_best_ckpts(LOGS_PATH, [f'model_scaling_n_d=16_n_a=16_run{i}' for i in range(1, 4)]),\n",
    "    32: get_best_ckpts(LOGS_PATH, [f'model_scaling_n_d=32_n_a=32_run{i}' for i in range(1, 4)]),\n",
    "    64: get_best_ckpts(LOGS_PATH, [f'model_scaling_n_d=64_n_a=64_run{i}' for i in range(1, 4)]),\n",
    "    128: get_best_ckpts(LOGS_PATH, [f'model_scaling_n_d=128_n_a=64_run{i}' for i in range(1, 4)]),\n",
    "    256: get_best_ckpts(LOGS_PATH, [f'model_scaling_n_d=256_n_a=64_run{i}' for i in range(1, 4)])\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "52d93879-6fcd-45d2-9616-ff2761a1a202",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reusing val_f1_macro_epoch=46_val_f1_macro=0.806.ckpt checkpoint: f1-score=0.7875\n",
      "Reusing val_f1_macro_epoch=44_val_f1_macro=0.807.ckpt checkpoint: f1-score=0.7855\n",
      "Reusing val_f1_macro_epoch=47_val_f1_macro=0.806.ckpt checkpoint: f1-score=0.7863\n",
      "Reusing val_f1_macro_epoch=45_val_f1_macro=0.829.ckpt checkpoint: f1-score=0.8103\n",
      "Reusing val_f1_macro_epoch=44_val_f1_macro=0.825.ckpt checkpoint: f1-score=0.8097\n",
      "Reusing val_f1_macro_epoch=45_val_f1_macro=0.825.ckpt checkpoint: f1-score=0.8079\n",
      "Reusing val_f1_macro_epoch=43_val_f1_macro=0.842.ckpt checkpoint: f1-score=0.8219\n",
      "Reusing val_f1_macro_epoch=46_val_f1_macro=0.842.ckpt checkpoint: f1-score=0.8235\n",
      "Reusing val_f1_macro_epoch=45_val_f1_macro=0.842.ckpt checkpoint: f1-score=0.8263\n",
      "Reusing val_f1_macro_epoch=43_val_f1_macro=0.848.ckpt checkpoint: f1-score=0.8290\n",
      "Reusing val_f1_macro_epoch=42_val_f1_macro=0.848.ckpt checkpoint: f1-score=0.8312\n",
      "Reusing val_f1_macro_epoch=44_val_f1_macro=0.847.ckpt checkpoint: f1-score=0.8309\n",
      "Reusing val_f1_macro_epoch=45_val_f1_macro=0.851.ckpt checkpoint: f1-score=0.8325\n",
      "Reusing val_f1_macro_epoch=44_val_f1_macro=0.850.ckpt checkpoint: f1-score=0.8312\n",
      "Reusing val_f1_macro_epoch=44_val_f1_macro=0.851.ckpt checkpoint: f1-score=0.8331\n"
     ]
    }
   ],
   "source": [
    "import gc\n",
    "\n",
    "\n",
    "n_hidden = []\n",
    "f1_score = []\n",
    "ckpt_list = []\n",
    "\n",
    "estim = EstimatorCellTypeClassifier(DATA_PATH)\n",
    "estim.init_datamodule(batch_size=4096)\n",
    "estim.trainer = pl.Trainer(logger=[], accelerator='gpu', devices=1)\n",
    "try:\n",
    "    res_df = pd.read_csv('model_eval_cache/model_scaling.csv', index_col=0)\n",
    "except FileNotFoundError:\n",
    "    res_df = pd.DataFrame({'n_hidden': [], 'f1_score': [], 'ckpt': []})\n",
    "\n",
    "\n",
    "for n_d, ckpts in CKPTS_MODEL_SCALING.items():\n",
    "    for ckpt in ckpts:\n",
    "        if ckpt in res_df.ckpt.tolist():\n",
    "            # use cached data if available\n",
    "            f1_ckpt = float(res_df.loc[res_df.ckpt == ckpt, 'f1_score'])\n",
    "            print(\n",
    "                f\"Reusing {ckpt.split('/')[-1]} checkpoint: f1-score={f1_ckpt:.4f}\"\n",
    "            )\n",
    "            ckpt_list.append(ckpt)\n",
    "            n_hidden.append(n_d)\n",
    "            f1_score.append(f1_ckpt)\n",
    "        else:\n",
    "            n_hidden.append(n_d)\n",
    "            ckpt_list.append(ckpt)\n",
    "            estim.model = TabnetClassifier.load_from_checkpoint(ckpt, **estim.get_fixed_model_params('tabnet'))\n",
    "            res = estim.test()[0]\n",
    "            f1_score.append(res['test_f1_macro'])\n",
    "            gc.collect()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7beec27f-e2fb-4cd7-9ed0-d8945f71105f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "res_df2 = pd.DataFrame({'n_hidden': n_hidden, 'f1_score': f1_score, 'ckpt': ckpt_list})\n",
    "res_df2.to_csv('model_eval_cache/model_scaling.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d7ceb0ae-65ce-4eff-adc5-23d414ca8c83",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "performance_linear = pd.read_csv('model_eval_cache/model_comparision.csv', index_col=0).query('model == \"linear\"')['f1-score (macro avg.)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9a33775f-fc1c-4c27-847b-f684874c424f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x275 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(1, 1, figsize=(5.5, 2.75))\n",
    "ax1.hlines(performance_linear, xmin=res_df2.n_hidden.min(), xmax=res_df2.n_hidden.max(), label='linear', lw=2, linestyles='dotted')\n",
    "\n",
    "sns.lineplot(\n",
    "    x='n_hidden',\n",
    "    y='f1_score',\n",
    "    marker='o',\n",
    "    data=res_df2,\n",
    "    ax=ax1,\n",
    "    color=sns.color_palette()[1],\n",
    "    label='scTab'\n",
    ")\n",
    "ax1.set_xlabel('number of hidden units')\n",
    "ax1.set_ylabel('F1-score (macro avg.)')\n",
    "ax1.set_xscale('log', base=2)\n",
    "xticks = np.unique(res_df2.n_hidden)\n",
    "ax1.set_xticks(xticks)\n",
    "ax1.set_xticklabels(xticks)\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('/dss/dsshome1/04/di93zer/git/cellnet/figure-plots/figure2/model-scaling.pdf')\n",
    "plt.savefig('/dss/dsshome1/04/di93zer/git/cellnet/figure-plots/figure2/model-scaling.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "220a520f-3f9d-454c-8512-c54a6a0232b9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>n_hidden</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.786417</td>\n",
       "      <td>0.001022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.809308</td>\n",
       "      <td>0.001244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>0.823915</td>\n",
       "      <td>0.002229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.830362</td>\n",
       "      <td>0.001166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>0.832272</td>\n",
       "      <td>0.000969</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              mean       std\n",
       "n_hidden                    \n",
       "16        0.786417  0.001022\n",
       "32        0.809308  0.001244\n",
       "64        0.823915  0.002229\n",
       "128       0.830362  0.001166\n",
       "256       0.832272  0.000969"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res_df2.groupby('n_hidden')['f1_score'].agg(['mean', 'std'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54062329-a176-4997-8db6-e183d3d245e0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
